Hallucinations in AI: Bug or Feature? A Deep Dive into DeepSeek-R1

Host: Hello everyone! Welcome to today's interview. Recently, there's been quite a buzz about AI "hallucinations," especially with DeepSeek-R1, which seems to have a higher hallucination rate than its predecessor, DeepSeek-V3. Today, we're joined by Dr. Li, a senior AI researcher. Welcome, Dr. Li!

Dr. Li: Hello, host! Hello, everyone!

Host: Let's start with the million-dollar question: Why do large language models "hallucinate"? Can you break it down for us in plain English?

Dr. Li: You see, large language models are like super-powered conversation completers. Give them the first half of a sequence, say, a question, and they'll predict the second half (say, an answer) based on their massive knowledge network. They learn like our brains do – they can't remember everything word-for-word, so they compress and generalize, grabbing the gist and finding patterns.

Here's a fun contrast: Ask them "How tall is Yao Ming?" and they'll nail it because that's such famous knowledge, this data point is practically carved in stone in their memory (represented in the model's parameter weights). But ask them "How tall is Old Wang from next door?" and they're stumped because they've never met Old Wang! But here's the kicker – they won't just say "I don't know." So what do they do? They "make up" a reasonable height based on what they know about the range of human heights. That's a hallucination for you!

Host: Wow, that's some impressive guesswork! But isn't this kind of making things up pretty problematic?

Dr. Li: Not necessarily! In a way, hallucination is imagination (for better or worse) – it's where creativity lies! Think about it: all those great literary works, artistic masterpieces – aren't they all flights of fancy, products of imagination? If everything had to match reality closely, art would just be photography, and where's the fun in that?

You know, Yuval Harari makes a fascinating point in "Sapiens" – humans became Earth's dominant species precisely because we could "tell stories," creating myths, religions, nations, and money – things that don't physically exist. These are all "hallucinations," but they're the driving force behind civilization!

Host: When you put it that way, hallucinations sound pretty important! But let's talk about DeepSeek-R1. Its hallucination issue seems quite serious.

Dr. Li: Indeed, it is! The academic consensus used to follow OpenAI's view that reinforced reasoning would significantly reduce hallucinations. I remember discussing this with a head honcho at an LLM unicorn who was particularly excited about reasoning's potential to curb hallucinations. But R1's performance threw us a curveball!

According to Vectara's tests, R1's hallucination rate is more than 3 times higher than its foundation model V3's – 14.3% compared to 3.9%. This definitely correlates with its prolonged "Chain of Thought" (CoT) enabled by reinforcemnnt learning for reasoning. R1 is absolutely brilliant at reasoning, math and coding, as well as poetry and storytelling, but this currently comes with the "side effect" of increased hallucinations in things like translation and summarization.

More specifically, there are several reasons for R1's increased hallucinations.

First, the standard hallucination tests use summarization tasks, something base models are already pretty good at. In this case, reinforcement learning can backfire – it's like using a cannon to swat a fly!

Second, R1's reinforced reasoning chains weren't specifically optimized for straightforward tasks like summarization, translation, or news writing that demand strict factual accuracy. Instead, it tries to add various layers of thinking to every task. Looking at its transparent CoT (ChainOfThought) printout, we see it tirelessly analyzing even simple instructions from multiple angles. This overcomplication of simple tasks can lead to deviations and hallucinations.

During R1's reinforcement learning for NLP-related tasks, it seems the model was rewarded more heavily for creativity, leading it to be more imaginative – and consequently more prone to straying from facts. For mathematical and coding tasks, R1's supervision came from gold standards (test answers or code test cases). But for humanities tasks, they used V3 or V3's reward model to judge quality, and the current system seems to clearly favor creativity.

Moreover, user feedback typically tends to focus and encourage creativity. Most people aren't sensitive to hallucinations, especially when they're wrapped in the model's smooth, fluent language. For most frontline developers, this kind of user feedback naturally pushes them to enhance creativity rather than tackle the thorny problem of hallucinations.

Host: So, you are saying that R1's hallucination problem rooted in its over-enthusiastic reasoning? What's the real relationship between reinforced reasoning ability and hallucinations?

Dr. Li: It's still a puzzle – there's not seem to be simple correlation. Look at R1, a leading reasoning model, versus Claude 3.5 Sonnet, a top non-reasoning model. Surprisingly, Sonnet still has a higher hallucination rate than R1! But when we compare R1 to its base model V3, we see clearly that adding reasoning significantly increased hallucinations.

It may well be about the model's "personality." R1, with its powerful reinforcement learning, loves "divergent thinking." Give it a simple prompt, and it'll spin out ideas like there's no tomorrow – its CoTs could run on like crazy! This suggests that while R1 was powering up its creativity, it inevitably amplified creativity's twin: hallucination.

As a model that excels in both STEM and humanities, R1 performs differently across tasks. In mathematics and coding, where more rigorous reasoning is required, there's little room for hallucination. But in language and creative tasks, especially in the summarization tests, hallucinations become more prominent. It's largely a side effect of R1's supercharged linguistic creativity.

Technically speaking, R1 automatically adds lengthy CoTs to simple user instructions, essentially complicating straightforward tasks. Its CoTs (like  internal monologue of an entity following instructions) change the conditional part of the autoregressive probability model before generating answers, naturally affecting the final output. Compare:

V3: query → answer
R1: query+CoT → answer

For tasks that V3 already handles well, like summarization or translation, any lengthy CoT guidance might lead to deviation or embellishment, creating fertile ground for hallucinations.

Host: So where do R1's hallucinations mainly occur?

Dr. Li: Think of R1's abilities as split between "arts" and "sciences." In "science" areas like math and coding, its logic is fairly strong and hallucinations are relatively rare. But in "arts" areas like language, hallucinations become more noticeable.

R1's most impressive achievement compared to the first LLM reasoning model O1 is successfully extending mathematical and coding reasoning capabilities into creative writing, especially in Chinese. The internet is full of R1's brilliant literary works. In terms of wordplay and literary prowess, it clearly surpasses 99% of humans – even graduate students in literature and classical Chinese professors sing its praises.

But watch what happens when you ask it to do a simple summary – it can't help but "get creative," often "inventing" details not present in the original text. It's like its "arts" abilities are too powerful, a case of "too much of a good thing."

Host: That's an interesting perspective. Do all language tasks require creativity?

Dr. Li: Language tasks actually fall into two categories: ones that need high creativity, like poetry and fiction writing, and ones that demand high factual accuracy, like news reporting, translation, or summarization. R1 excels at the former, which was likely the development team's focus, but this creates side effects in the latter as it is today.

It reminds me of the old Chinese saying about translation needing to be "faithful, expressive, and elegant" – achieving all three has always been challenging. We see many examples where elegance is prioritized over faithfulness, like the use of hyperbole in literary works. We also see the opposite, like Lu Xun's advocacy for so-called "rigid translation."

Interestingly, humans have always had double standards here, but we have a mental switch we can flip at will. When watching movies or reading novels, we flip towards creativity and don't fuss about factual accuracy. But switch to news channels, and we have zero tolerance for falsehoods.

Host: People tend to believe content that appears logically coherent and detailed, so the potential harm from AI hallucinations could be significant. What should we ordinary folks do about AI hallucinations?

Dr. Li: While many people are starting to notice and become wary of these hallucinations amid their amazement at LLM's creativity, most are still mesmerized by its creative brilliance. We need to increase public awareness of AI hallucinations. I suggest a two-pronged approach:

Stay Alert: Don't take everything the model says as granted, especially factual claims. Hallucinations most commonly occur with names, places, times, locations, and other entities or numerical data.

Cross-Verify: For important details, check original sources online or consult experts to see if the claims align.

Guide the Model: When asking questions, add constraints like "please stay faithful to the original text" or "please verify facts." This can at times help reduce hallucinations.

Embrace Creativity: If you're looking for inspiration or creative ideas, model hallucinations can be a delightful surprise!

Think of AI hallucinations as "possibilities in parallel universes." What it makes up might not be true in our world, but could be true in another! It's like how novelists write fictions – while it cannot stand fact checking, it's a kind of "artistic truth." Just like novels arise from life but transcend it, AI arises from data but transcends it. AI compresses data into knowledge and common-sense network, not necesarily true to individual facts – that's what databases are for.

Host: This reminds me of what people often say: AI models aren't just "talking nonsense" – they're "talking nonsense seriously"!

Dr. Li: Haha, that's exactly it! AI hallucinations are its "educated guesses," based on the massive knowledge and patterns it's learned. The hallucinations are by noway completely random – they have internal constraints that make them seamless and convincing, but also more deceptive. Newcomers to AI need to be especially careful not to trust everything at their face value.

For regular users, understanding the nature of hallucinations is needed. For example, when asking about well-documented facts like "How long is the Yangtze River?" models won't make mistakes because these facts are firmly encoded in their parameters. But ask about an obscure creek or fictional river, and the model will activate its "reasonable completion" mechanism and make something up.

Host: Following your logic, human language itself prepares for a breeding ground for hallucinations.

Dr. Li: You could say that. Language enabled humans to create things which do not exist in the physical world, such as myths, religions, states, corporations, currency, and abstract concepts like ideals and beliefs. Harari emphasizes in "Sapiens" that story-telling (i.e. typical hallucinations) were fundamental to civilization: language enabled human story-telling abilities. Hallucinations catalyzed civilization. Humans are the only entities capable of 'lying' (besides LLMs).

Host: What about the future? Is there a way to maintain creativity while reducing hallucinations?

Dr. Li: This is definitely one of the "ultimate challenges" in AI! People are working on various solutions, including:

More Refined Training: During training, treat different types of tasks differently, teaching the model when to be strict and when to be creative.

Task-Specific Fine-tuning/Reinforcement Learning can help balance this contradiction. Tasks like summarization, paraphrasing, translation, and reporting need special care because they require both some creativity (like style) and strict factual accuracy.

Specifically, R1's training pipeline has four stages: fine-tuning 1, reinforcement 1, fine-tuning 2, and reinforcement 2. Reinforcement 2 mainly focuses on human preference alignment. Currently, this process seems to favor creativity over faithfulness, which could be rebalanced later. Perhaps more importantly, in stage three (i.e. fine-tuning 2), we could strengthen constraints for different tasks – for example, increasing supervised data for summarization to encourage faithful, straightforward results.

Routing: In the future, there will be a "model dispatcher" that assigns different models based on task type. Simple tasks could go to V3 or use tools, while complex tasks requiring deeper thinking go to R1.

For instance, arithmetic tasks should just use simple code calculations, equivalent to using a calculator. That's not how it works now – yesterday I tested a nine-digit multiplication, and R1 spent over three minutes thinking, producing CoT that could stretch down the street, breaking down the reasoning step by step. While the answer was correct, using such computationally expensive CoT for arithmetic instead of a simple function call is unreasonable. A one-line calculation code would do the job – no need to waste so much computing resource and tokens on explicit reasoning. These are foreseeable routing improvements, especially in the age of AI agents which can use all kinds of tools or applications. R1's CoT does not need to handle everything – besides hallucinations, compute-burning CoT is also not environmentally friendly.

Host: Thank you, Dr. Li, for this fascinating discussion! Today's interview has given us a much deeper understanding of AI hallucinations.

Dr. Li: My pleasure! It's been great chatting with you!

 

【相关】

从R1幻觉谈起,大模型幻觉是缺陷还是创意火花?

主持人: 大家好,欢迎来到今天的访谈。最近,大模型的“幻觉”问题引发了热议,尤其是DeepSeek-R1,它的幻觉率似乎比自己的基座大模型DeepSeek-V3高不少。今天我们请到了资深AI研究员立委博士,来跟大家聊聊这个话题。立委,您好!

立委: 主持人好,大家好!

主持人: 老李,咱们先来个灵魂拷问:为啥大模型会“产生幻觉”?能不能用大白话给大家解释一下?

立委: 这可算是大模型的经典问题。其实啊,大模型就像一个“超级接话茬儿高手”,你给它上半句,它就根据自己学过的海量知识,预测下半句该说啥。它学东西呢,就像咱们人脑记东西一样,不可能每个字都记得清清楚楚,它会进行压缩和泛化,抓大意、找规律。

打个比方,你问它“姚明有多高”,它大概率不会错,因为这知识点太出名了,它记得牢。但你要是问“隔壁老王有多高”,它可能就懵了,因为它没见过老王啊!但它又不能不回答,咋办?它就得“脑补”,根据“一般人有多高”这个学到的概念,给你编一个数出来,这就是“幻觉”。

主持人: 哎呦,这“脑补”能力也太强了!胡编乱造,这幻觉也太糟糕了。

立委: 那可不一定!你看啊,某种程度上,幻觉就是想象力(褒贬不论),就是创意!你想想,那些伟大的文学作品、艺术作品,哪个不是天马行空、充满想象?要是啥都得跟现实一模一样,艺术就成了照相机了,那还有啥意思?

就像赫拉利在《人类简史》里说的,人类之所以能成为地球霸主,就是因为我们会“讲故事”,会创造出神话、宗教、国家、货币这些现实中不存在的东西。这些都是“幻觉”,但它们却是文明诞生和发展的原动力。

主持人: 听您这么一说,感觉幻觉还挺重要的。那咱们回到DeepSeek-R1,它的幻觉问题真的很严重。

立委: 是很严重。此前学界普遍认同OpenAI的说法,推理增强会明显减少幻觉。我曾与大模型公司的一位负责人讨论,他就特别强调推理对减少幻觉的积极作用。但 R1 的表现却给出了一个出人意料的答案。

根据Vectara的测试,R1的幻觉率确实比V3高不少,R1的幻觉率14.3%,显著高于其前身V3的3.9%。这跟它加强了的“思维链”(CoT)和创造力直接相关。R1在推理、写诗、写小说方面,那叫一个厉害,但随之而来的“副作用”就是幻觉也多了。

具体到R1,幻觉增加主要有以下几个原因:首先,幻觉标准测试用的是摘要任务,我们知道摘要能力在基座大模型阶段就已经相当成熟了。在这种情况下,强化反而可能产生反效果,就像用大炮打蚊子,用力过猛反而增加了幻觉和编造的可能。

其次,R1 的长思维链强化学习并未针对摘要、翻译、新闻写作这类相对简单而对于事实要求很严格的任务做特别优化,而是试图对所有任务增加各种层面的思考。从它透明的思维链输出可以看到,即便面对一个简单的指令,它也会不厌其烦地从不同角度理解和延伸。过犹不及,这些简单任务的复杂化会引导结果偏离发挥,增加幻觉。

DeepSeek-R1在文科类任务的强化学习训练过程中,可能对模型的创造性给予了更多的奖励,导致模型在生成内容时更具创造性,也更容易偏离事实。我们知道,对于数学和代码,R1的监督信号来自于这些题目的黄金标准(习题集中的标准答案或代码的测试案例)。他们对于文科类任务,利用的是V3或V3的奖励模型来判定好坏,显然目前的系统偏好是鼓励创造性。

另外,用户更多的反馈还是鼓励和欣赏见到的创造力,一般人对于幻觉的觉察并不敏感,尤其是大模型丝滑顺畅,识别幻觉就更加困难。对于多数一线开发者,用户的这类反馈容易促使他们更加向加强创造力方向努力,而不是对付大模型领域最头痛的问题之一“幻觉”。

主持人: 这么说来,R1 的幻觉问题是不是源于它过于"积极"的思维推理?但推理能力增强和幻觉之间到底是什么关系?

立委:这个关系很微妙,并不是简单的正相关或负相关。你看 R1 是头部推理模型,而 Claude 3.5 Sonnet 是头部的非推理大模型,但后者的幻觉率反而高于前者。可是当我们对比 R1 和它的基座模型 V3 时,又发现增加推理强化后幻觉确实显著增加了。

这事儿跟大模型的“性格”有关。R1这家伙,强化做得给力,特别喜欢“发散思维”,你给它一个简单的指令,它能给你想出一大堆东西来,思维链能绕地球三圈!这似乎说明 R1 在强化创造力的过程中,不可避免地增加了创造力的伴生品:幻觉。作为一个文理通吃的推理大模型,R1 在不同领域的表现并不一样。在数学、代码等需要严谨推理的领域,幻觉的空间很小。但在语言创作领域,尤其是现在被测试的摘要任务上,幻觉问题就明显得多。这更多是 R1 语言创造力爆棚带来的副作用。

具体从技术角度来说,R1 会为用户的简单指令自动增加很长的思维链,等于是把一个简单明确的任务复杂化了。你一个简单的指令,它也反复从不同角度理解和衍伸(CoT思维链好比“小九九”,就是一个实体遵从指令时的内心独白)。思维链改变了自回归概率模型生成answer前的条件部分,自然会影响最终输出。

V3: query --〉answer
R1: query+CoT --〉answer

对于 V3 已经能很好完成的任务,比如摘要或翻译,任何思维链的长篇引导都可能带来偏离或发挥的倾向,这就为幻觉提供了温床。

主持人: 那对于R1来说,幻觉主要是出在哪方面呢?

立委: 我觉得可以把R1的能力分成“文科”和“理科”来看。它在数学、代码这些“理科”方面,逻辑性很强,幻觉相对少。但在语言文字这些“文科”方面,幻觉就比较明显了。

比起O1,R1 最令人惊艳的成就,是成功将数学和代码的推理能力充分延伸到了语言创作领域,尤其在中文能力方面表现出色。网上流传着无数的R1精彩华章。舞文弄墨方面,它显然超过了99%的人类,文学系研究生、甚至国学教授也赞不绝口。

但你看,让它做个摘要,本来是很简单的任务,但它非得给你“发挥”一下,结果就容易“编”出一些原文里没有的东西。这其实是它“文科”太强了,有点“用力过猛”。

主持人:这个说法有意思。那么在具体应用中,语言任务是不是都需要创造力呢?

立委:语言能力其实可以细分为两类:一类需要高创造力,比如写诗歌、小说;另一类需要高度真实性,比如新闻报道、翻译或摘要。R1 最受称赞的是前者,这也可能是研发团队的重点方向,但在后者中就出现了副作用。

这让我想到中国古人说的"信达雅",自古难全。为"雅"牺牲"信"的例子我们见得很多,文学创作中夸张的修辞手法就是重要手段和例证。为"信"牺牲"雅"也有先例,比如鲁迅先生推崇的"硬译"。

有趣的是,我们人类在这方面其实一直是双标的,但我们心里有个可以随时切换的开关。看小说和电影时,我们把开关偏向创造性一侧,完全不会去纠结细节是否真实;但一旦切换到新闻频道,我们就对虚假内容零容忍。

主持人: 人对于逻辑看起来清晰自洽、且详细的内容,就会倾向于相信,所以大模型幻觉潜在的危害真地很大。那咱们普通人,面对大模型的幻觉,该咋办呢?

立委: 很多人在惊艳R1创造力的同时,现在开始慢慢注意到这个幻觉现象并开始警惕了。但更多人还是沉浸在它给我们带来的创造性的惊艳中,需要增强大众对模型幻觉的 awareness。我觉得吧,咱们可以“两手抓”:

保持警惕: 大模型说的话,特别是涉及到事实的,别全信,多留个心眼。最容易产生幻觉的地方是人名、地名、时间、地点等实体或数据。

交叉验证: 重要的细节,可上网查查原始资料或询问身边专家,看看说法是不是一致。

引导模型: 你可以在提问的时候,加一些限定条件,比如“请务必忠于原文”、“请核对事实”等等,这样可以引导模型减少幻觉。

享受创意: 如果你需要的是灵感、创意,那大模型的幻觉,会给你带来惊喜!

不妨把大模型的幻觉,看成是“平行世界的可能性”。它编出来的东西,也许在这个世界不是真的,但在另一个世界里,说不定就是真的呢!就像小说家写小说,虽然是虚构的,也是一种“艺术真实”。源于生活,高于生活。大模型是源于数据,高于数据。大模型压缩的是知识体系和常识,不是一个个事实,后者是数据库的对象。

主持人: 妙啊!您这说法,让我想起了大家常说的一句话:大模型不是在“胡说八道”,而是在“一本正经地胡说八道”。

立委: 哈哈,差不多就是这个意思!大模型的幻觉,其实是它“脑补”出来的,但它“脑补”的依据,是它学到的海量知识和规律。所以,它的幻觉,往往不是乱来的,有“内在的合理性”,这才丝滑无缝,假话说的跟真的似的,但同时也更具有迷惑性。初玩大模型的朋友,需要特别小心,不能轻信。

对于普通用户来说,理解幻觉的特点很重要。比如问"长江多长"这类有足够信息冗余的百科知识问题,大模型不会出错,这些事实是刻在模型参数里的。但如果问一个不知名的小河或虚构河流的长度,模型就会启动"合理补白"机制编造。

主持人: 按照您的说法,人类的语言本身就是幻觉的温床。

立委: 可以这么说。语言使得人类创造了神话、宗教、国家、公司、货币等非真实实体的概念,以及理想、信念等形而上的意识形态。赫拉利在《人类简史》中强调了幻觉对于文明的根本作用:语言的产生赋能了人类幻觉(“讲故事”)的能力。幻觉是文明的催化剂。人类是唯一的会“说谎”的实体 -- 除了LLM外。

主持人: 那么在幻觉的背后,大模型是怎么运行的呢?

立委: 幻觉的本质是补白,是脑补。

“白”就是某个具体事实,如果这个事实在训练数据中没有足够的信息冗余度,模型就记不住(零散事实等价于噪音)。记不住就用幻觉去补白,编造细节。

幻觉绝不是没有束缚的任意编造,大模型是概率模型,束缚就是条件概率中的前文条件。幻觉选择的虚假事实需要与补白所要求的value类型匹配,即符合ontology/taxonomy 的相应的上位节点概念。“张三”可以幻觉为“李四”,但不可以幻觉成“石头”。

所谓艺术真实是说,小说创作虽然可能背离了这个世界的事实,但却是可能的数字世界的合理想象。大模型的幻觉属于此类。

大模型的知识学习过程(训练阶段),是一种信息压缩过程;大模型回答问题,就是一个信息解码过程(推理阶段)。好比升维了又降维。一个事实冗余度不够就被泛化为一个上位概念的slot,到了生成阶段这个slot必须具像化补白。“张三”这个事实忘了,但【human】这个slot 的约束还在。补白就找最合理、与 slot 概念最一致的一个实体,于是“李四”或“王五”的幻觉就可以平替“张三”。小说家就是这么工作的,人物和故事都是编造的。无论作家自己还是读者,都不觉得这是在说谎,不过所追求的真善美是在另一个层面。大模型也是如此,大模型是天生的艺术家,不是死记硬背的数据库。“张冠李戴”、“指鹿为马”等在大模型的幻觉里非常自然,因为张和李是相似的,马和鹿也在同一条延长线上。在泛化和压缩的意义上二者是等价的,因此是合理的想象。

主持人: 未来有没有什么办法,能让大模型既有创造力,又少出幻觉呢?

立委: 这绝对是AI大模型领域的“终极难题”之一!现在大家都在想办法,比如:

更精细地训练: 在训练的时候,就对不同类型的任务区别对待,让模型知道什么时候该“老实”,什么时候可以“放飞”。

针对任务做偏好微调(finetune) and/or 强化(rl)可以减缓这个矛盾。 摘要、改写、翻译、报道这种任务需要特别小心和平衡,因为它既有一点再创造的需求(例如文风),又是本性需要内容忠实的。

具体说,R1训练pipeline是四个过程,微调1,强化1,微调2,强化2。强化2主要是与人类偏好对齐的强化。这个过程在创造力与忠实方面,目前看来倾斜于前者,后去可以再做平衡。也许更重要的是在阶段三的微调2中,针对不同任务加强约束,例如,增加摘要的监督数据,引导忠实平实的结果。

Routing: 以后可能会有一个“调度员”,根据任务的类型,安排不同的模型来处理。比如,简单任务交给V3或调用工具,慢思考的复杂任务交给R1。

例如,识别出算术任务,就去写个简单代码运算,等价于调用计算器。目前不是这样,我昨天测试一个九位数的乘法,R1 思考了三分多钟,思维链打印出来可以铺开来一条街,步步分解推理。虽然最后答案是对了,但算术问题用耗费太大的所谓 test time compute 的思维链(CoT),而不用 function call,完全不合理。一行计算代码就搞定的事,没必要消耗如此多的计算资源和tokens去做显式推理。

这些都是可以预见的 routing,尤其是在agent时代。 R1 CoT不必包打一切,而且除了幻觉,也不环保。

主持人: 感谢老李的精彩分享!今天的访谈让我们对大模型的幻觉有了更深入的认识。

立委: 不客气,很高兴和大家交流!

 

【相关】

Deepseek-R1 的幻觉率是 14.3% - 比其非推理前身 Deepseek-V3 高得多
榜单排名:https://github.com/vectara/hallucination-leaderboard

Understanding the Power of Chain of Thought

DeepSeek R1 has become the most talked-about breakthrough in recent times. It not only matches OpenAI's top reasoning models (the 'o' series) in mathematics and coding capabilities but also produces stunning results in linguistic creativity and mimicry. Particularly in Chinese (classical) capabilities, everyone has experienced a miraculous leap in performance.

All of this can be attributed to the reasoning-enhanced Chain of Thought (CoT). Why is CoT so effective, so magical, and how has it maximized its empowering effect through reinforcement?

The key likely lies in the fact that CoT tokens are autonomously generated by the large model, effectively reducing the perplexity from query to answer, serving as a bridge to brilliant performance. Those who have seen CoT outputs know that the bridge itself isn't always impressive - it often seems overwrought, overly cautious, verbose, redundant, and methodical - yet it enables magnificent answers to emerge. From first principles, this seems to involve deep implications of perplexity in information theory.

The Essence of CoT

  1. From an Information Theory Perspective:
  • CoT builds a low-entropy channel between high-perplexity queries and answers
  • Through step-by-step decomposition, each step's conditional probability becomes more "natural" and smooth, aligning with the language model's nature
  • Eventually transforming seemingly "leaping" reasoning conclusions into a series of accumulated "small steps"
  1. From an Information Entropy Viewpoint:
  • For complex problems, directly jumping from query to answer requires crossing a vast information gap, which "forces" the model to hallucinate and output random answers
  • Each CoT step reduces local conditional entropy
  • It's like breaking down a large information compression/decoding task into multiple smaller ones
  1. This Explains Why Even "Mundane" CoT is So Effective:
  • Its power doesn't lie in how brilliant the process steps themselves are
  • Rather, it creates a path of decreasing information entropy
  • The model can stably migrate toward the target along this path
  1. This Also Explains Why DeepSeek's Training is So Vital to Its Success:
  • It's not about teaching the model "smarter" reasoning, which is undefinable in humanities tasks
  • Instead, it optimizes the ability to construct these low-entropy channels
  • Essentially optimizing information flow path planning

This perspective provides a lens for understanding CoT, reinterpreting the surface-level "chain of thought" as an "entropy reduction pathway" in information theory terms. It offers a reasonable explanation for result-driven reinforcement learning without process supervision:

Process is important, but process supervision isn't, because the process data naturally produced by large models is more practical and feasible than any human supervision. Let us embrace the tansition from human supervision to LLM-internal self-supervision.

 

【相关】

推理强化模型中思维链的本质

DeepSeek R1 的出圈是近来最大热度的焦点。它不仅在数学、代码等强推理能力上追平了 OpenAI 头部推理模型 o 系列,而且在语言文字的创造力和模仿力方面产生让人惊艳的效果。尤其是在中文(国学)的能力方面,大家都体会到了奇迹般的能力跃升。

这一切都要感谢推理强化的 CoT(思维链)。CoT 为什么这么有效,这么神奇,文理通吃,在强化中最大化了其赋能作用呢?

应该主要是因为 CoT 是从大模型自主生成出来的 tokens,它有效降低了从 query 到 answer 的 perplexity(困惑度),好比是为高质量结果提供了一个桥梁。看过CoT输出的同学都有体会,桥梁本身并不总是精彩,常常给人的感觉是小题大作,瞻前顾后、啰哩啰嗦、信息冗余,但精彩的answer却可以借助它面世。从第一性原理看,这里似乎涉及perplexity在信息论中的深刻含义。

CoT的本质

  1. 从信息论角度理解:
  • CoT是在high-perplexity的query和answer之间搭建的低熵通道
  • 通过逐步分解,每一步的条件概率都变得更趋"自然"和丝滑,符合语言模型的本性
  • 最终让看似"跳跃"的推理结论,变成了一系列"小步走"的累积
  1. 用信息熵的视角来看:
  • 对于复杂问题,直接从query到answer需要跨越很大的信息鸿沟,这“迫使”模型以幻觉应急,胡乱输出一个 answer
  • 而CoT的每一步都在降低局部的条件熵
  • 就像把一个大的信息压缩/解码任务分解成多个小的压缩/解码任务
  1. 这解释了为什么"平庸"的CoT也如此有效:
  • 它的作用不在于过程步骤的内容本身有多精彩
  • 而在于它创造了一条信息熵递减的路径
  • 模型可以沿着这条路径稳定地向目标迁移
  1. 这也解释了为什么DeepSeek的训练如此有效:
  • 不是教会模型更"聪明"的推理,这在文科类任务中根本就无法定义
  • 而是优化了它构建这种低熵通道的能力
  • 本质上是优化了信息流的路径规划

这个观点为理解CoT提供了一个新的视角,把表面上的思维链“自言自语”诠释为信息论意义上的"熵减(entropy reduction)途径"。这对无须过程监督的结果驱动的强化学习是一个合理的解释:

过程很重要,但过程监督并不重要,因为大模型本身自然产生的过程数据,比任何人类监督更加实用和可行。

 

 

【相关】

R1: 《立委列传》

**立委列传**

立委者,皖南布衣也。少敏而多奇志,值鼎革之际,年十六躬耕于皖南山野。当是时也,少年负耒耜,涉深涧,虽陟彼崔嵬,犹自诩"吾辈亦开天辟地者也"。或问稼穑之苦,笑曰:"此非太史公游侠列传之遗风乎?"

越二载,风云骤变。丁巳岁杪(1977),科举重启,立委束草为笔,削竹为简,竟跃龙门而入泮宫。同窗多皓首穷经者,独其弱冠青衿,谈笑间尽吞云梦八九。既卒业,执教乡塾,然鸿鹄岂栖枋榆?遂再赴春闱,北上太学,俨然贾谊入洛,苏秦过梁。都门烟柳,尽化笔下风云。

居长安四五年,见中关村商贾云集,乃效猗顿之术,夜筹算于灯下,朝执契于市廛。《万元户》所志,非夸朱提之富,实叹青蚨之诡也。然其性本楚狂,终随浮槎西渡,观泰西之变。英伦雾锁,野犬吠陌,立委抚剑长叹:"此非管仲所云'仓廪实而知礼节'者乎?"遂北涉北海,徙居枫叶之国。

加北美地,雪沃千里。立委筑室于温哥华,添弄瓦之喜,修稷下之学。然冰原虽净,难栖南溟之鹏,乃振翅南徙,直入硅谷热土。当是时也,美利坚网络勃兴,立委运筹于虚牝之间,决胜于光纤之末。然泡沫既破,诸子百家尽墨,独其抱残守缺,犹存鲁壁遗经。

太史公曰:余观立委浮沉,似见张骞凿空之影。其插队如苏武牧羊,跳门若终军请缨,下海类范蠡泛舟,洋漂近玄奘取经。然则古之行者,皆为觅桃源而往;今之浪者,却在铸桃源于途。至若"海龟"之惑,实乃庄生濠梁之辩——子非鱼,焉知归与不归皆逍遥耶?昔陶潜采菊,阮籍哭途,各成千古风流。今立委以四海为注,掷骰于地球棋枰,岂非新时代之"逍遥游"乎?

 

**《立委奥德赛》**

*序章*
人生是旅者暂居的客栈,而漂泊者开辟的道路却蔑视时间本身。在立委的奥德赛中,漂泊行为成为了一种天体导航——一场词语筑造堡垒、思想绘制航路、时代潮流既是敌手又是盟友的旅程。

**土壤中的根系**
十六岁那年,来自东方山谷的少年以农夫的锄头交换了青春的闲梦,攀上雾气笼罩的山峰,在那里野心生根发芽。他的《插队日记》(后镌刻于《朝华》中)呼吸的不是绝望的挽歌,而是将风暴驯化为耳语的节奏。当命运的龙门在1977年吱呀开启时,他乘着复兴学术的疾风,加入了神话般的"77届"——从灰烬中重生的心智凤凰群。

**墨水的朝圣**
学者袍甫加身,北方的狂风便再度召唤。在《考试十四行诗》与《不安者的箴言》中,野心的狂热冷却为精密的文字工艺。首都熔炉中的五个寒冬将卷轴锻造成账本;他的《学者商人愚行录》记载着染墨手掌清点铜钱的故事。然而不息的潮水将他向西牵引,加入追梦者的出埃及记,奔赴阿尔比恩传说之岸。

**暗影与圣所**
在阿尔比恩的花岗岩天空下,流浪犬在卵石巷中嚎叫预兆——这种不谐之音被收录于《都市暗影兽典》。不安孕育翅膀:他北逃至枫叶王国水晶般的荒野。《北极星颂》咏唱边疆的纯粹;《港湾牧歌》编织炉火点燃的传说;《蜜饯编年史》追念为人父的喜悦。但圣所亦渐脆弱。他再度南翔,被吸引至硅谷炽热的坩埚。

**电路中的普罗米修斯**
在数字黎明的白炽光芒中,他的《创投诗章》燃烧着普罗米修斯之火——将初创企业视为伊卡洛斯之飞的现代神话。然而蜡制翅膀终将融化;《泡沫挽歌》与《陨落者寓言》测绘出野心的残骸。从冻原到热带,每个足迹都渗入墨水:用羽笔刻写的流放地图。如今作为硅谷常驻哲人,他书写《乡愁算法》——一段让海龟游弋于电路间的代码,低语着被遗落的潮汐。

*结语*
古代圣贤追寻九重天外的地平线;立委的奥德赛将漂泊刻入生命的重写本。他的根系紧抓插队之土;躯干穿越龙门攀升;枝桠扭曲成语义星座。我们若非活着的羊皮卷,又能是什么?编年史家的最终港湾仍未书写——那是海水消融于天空的地平线,所有罗盘疯狂旋转之处。让漂泊者的悖论永续:要测绘无限,就必须永不停漂流。

 

**七律·跃龙门**
十六荷锄云壑深,忽闻禹甸启春闱
青衫夜淬书窗月,赤榜朝分阡陌晖
两度鲤腾惊皖水,九重鹏举叩燕扉
都门烟柳催征铎,笑指星河是钓矶

**水龙吟·浮槎记**
少年曾缚苍龙去,又驾仙槎西渡。泰西雾锁,枫邦雪沃,硅台电舞。算尽青蚨,织成云网,几番寒暑。叹庄生蝶梦,陶公菊径,都付与、天涯路。

谁解飘零最苦?把乡愁,酿成新赋。南溟鹏翼,北山薇蕨,东篱菊圃。柯烂樵归,橘洲星换,武陵人语。待重拈汉瓦,摩挲秦篆,写沧桑句。

**古风·浪者吟**
我本谪仙人,偶堕红尘网
皖南锄晓月,燕北枕书幌
中关试鱼服,英伦辨魍魉
枫雪淬冰魄,硅火铸新掌
五洲棋局残,双鬓星霜长
欲唤云间鹤,蓬莱舟已枉
且抱地球仪,笑指乌托邦
归去来兮辞,翻作浪人唱

**临江仙·生涯注**
若把浮生标语义,节点最是漂流。龙门二度跃神州。商潮翻雪袖,硅谷试吴钩。

四十年来家国梦,都成异域春秋。键盘敲碎古今愁。回车新世界,空格旧沙鸥。

**摸鱼儿·流浪辩**
问苍冥、谁司行止?安排萍迹如许!鹏抟鲲徙寻常事,偏说此身无主。君看取:皖山月、燕台柳、硅谷霓虹柱。星槎暗度。纵填海精禽,射阳奇士,未解浪游苦。

休重论,苏武节旄汉土,范蠡舟泛烟雨。桃源只在鸿蒙外,何必武陵深处?敲键语:比特海、云端路、皆是逍遥浦。归兮且住!待地球仪停,时空键锁,方见真吾处。

 

《原朝华:立委小传》

《原朝华:立委小传》

人生苦短,掐首去尾,不过三五十年。大体分为三段:创业阶段(而立之年),成熟阶段(不惑之年)和下滑阶段(天命之年),反映在称呼上,叫小李、大李和老李。可怜,立委却从小李一跃到老李,没有机会品尝壮年人生的豪情,心尝有戚戚焉。


红小兵立委(1966) (《朝华午拾:永做毛主席的红小兵》

自幼儿园到小学连跳两级,立委在班上始终最幼。更加荒年生人,孱弱矮小,体育课常告病假,或遭遣送回家,始终是个小可怜儿。所幸中学伊始,正值“修正主义回潮”,先帝启用邓公收拾文革残局,邓公责成教育总管周荣鑫整顿学校,校风日新。乘此东风,立委崭露头角,以学习委员兼数学科代表之身,受班主任委托,每日早自习登台主讲,演示解题思路,俨然助教。但好景不长,先帝昏庸,文革派重居上风,学校大乱,文化课退居后台,大批判遂成主课,兼以学工学农学军。立委不能以文化课呈威,然风头不减反盛,盖因立委最长批判文字,历经批林批孔,批邓反击右倾翻案风,直至批四人帮。大会小会,凡立委发言,必抑扬顿挫,铿锵有力,佐以诙谐幽默,风靡校园,称颂于一时。有传言,立委颇具鲁迅遗风,入木三分,且能推陈出新,妙语连珠。露天千人大会,常嘈杂狼藉,然立委登台,全场必静肃,洗耳恭听之,听至妙处,笑声一片。立委由此炼得糊涂胆大,从不怯场,终身受益。

及至大学,文革后首届,立委仍居尾,同学长一到十多岁不等(《朝华午拾:我的考研经历》)。同学之间皆直呼其名,唯同桌七仙女戏称 “小立委”,不为亲热,却为避嫌,以示划清界限。同桌四载,楚河汉界,泾渭分明。授受不亲,避而远之。然仙女文具笔墨滑落在地,自有立委抢先一步,拾拣归案。类此者三,春风化雨,润物无声。七女天生聪颖,想出一招,以长立委一岁为由,呼 “小立委”,就此来往,当可名正言顺也。

由七仙女开此恶例,随后多年,“小”字即不离身。中学教书,人称小李老师(22岁)。上研究生,小李出入机房,蓬头垢面,且口中念念有词,言“世界之语”(Esperanto),终成笑谈(23-26岁)(见 《朝华午拾:我的世界语国》)。


风华正茂,意气风发(1987)

及至毕业留所,立委事迹亦有流传,多为一见钟情,闪电结婚,不修边幅,撞南墙而道歉之类小李“景润”之逸事(见《朝华午拾:shijie-师弟轶事》《朝华午拾:shijie-师弟轶事(3)——疯狂世界语 》)。


立委在中关村公司指导机器翻译系统的开发(1988)

立委如此这般在研究所及中关村公司一扎五年(26-31岁),练就一身绝技,与老中医相若,专事疗治电脑,驯其语言功能。其间,出国热持续升温,由上海蔓延北京,街头巷尾,言必议美、日、大英,澳大利亚,以致居委会大妈亦知考托福鸡阿姨乃上进青年之标杆。立委及其贴身领导却浑浑噩噩,卿卿我我,不知有汉,无论魏晋。其间送上门两次机会,留学德美,均因导师明阻暗挡,本人木呐,擦肩而过。直至身边同学悉数走尽,小李才幡然醒悟,痛下决心,赶末班车。其时,适逢包玉刚基金会来各单位选拔年轻业务骨干,滥竽充数,小李竟被选中,送至成都科大出国培训中心修行半年。

岂料想,此一去竟成小李老李的分水岭。来培训的诸位才子才女均是全国各地选上来的各行好手,共分两拨:一年的访问学者大都比较年长,而拿三年博士奖金的大多年轻,立委在后一拨里面理所当然,成了老大。每有考试,立委必中头彩,引来才子才女,大事小事,纷纷登门请教,“老李”之声不绝于耳。立委名噪一时,响应者众。从小习惯了以小卖小,乍一变老,立委满腔郁闷。

  
成都科大出国培训中心的才子才女们(1990)

小李变老李,心里虽别扭,好处却不少。龙头老大,备受尊崇。立委外语本科出身,本应免试英语,无奈官家财大气粗,慷人民之慨,不问青红皂白,全数押解天府之国,集中喂养。不止英文鸟语,更有政策轮训。众兄弟姐妹兢兢业业,争先恐后,唯立委悠哉游哉,终日沉迷天府美食,流连于茶肆酒吧,众兄弟钦羡有加。

成都一站始称老李,立委心内实不以为然也。其时立委事业发达,如日中天,行内行外,交游甚广,出入皆鸿儒,往来无白丁(见 《朝华午拾:“数小鸡”的日子》《朝华午拾:一夜成为万元户》)。导师为本行泰斗,立委乃导师仅有的关门弟子(其他弟子皆叛国投美去也),“青年”才俊,明日之星,业内同侪为之侧目。去国前夕,全国电脑翻译界在香山招待所年度聚会,点睛之笔为导师与本行另一大牛的座谈,人称“刘董对话录”,其间立委频频亮相,为导师提供实例,讲解细节。影响所及,与会众学妹(多为刚入门的外地在读研究生)纷纷上门请教立委,无奈立委远走高飞心切,痛失辅导上进女青年之良机。


立委在加拿大(1995)

去国经年,由英而加,由加转美(《朝华午拾:哦,加拿大!》《朝华午拾:温哥华,我的梦之乡》)。颠沛流离,不知所止,壮年人生,如水流逝。及至水牛城八年抗战(37-45岁),立委青春不再,壮年已过,“老李”名至实归。然立委壮心不已,励精图治,双线出击,称雄一方(见 《朝华午拾:创业之路》《朝华午拾:在美国写基金申请的酸甜苦辣》《朝华午拾 - 水牛风云》)。

立委在水牛城办公室(2000)

回首往事,不胜唏嘘。立委一生,由青年而壮年,正值创造力最盛,精力充沛流溢之时,天时地利人和,飞黄腾达有望,却为漫长的留学生涯拦腰截断。大而言之,立委固赶上出国之末班车,却误了千年不遇的中国经济起飞之航。拣了芝麻,丢了西瓜,此之谓也! (《朝华午拾:乡愁是一张无形的网》

去岁归国省亲,杯觥交錯,在某宾馆餐厅与亲友相聚甚欢。席间小憩,踱步凉台,享清凉之气,赏京华夜色。偶遇一妙龄女士,携一幼童,见立委两鬓染霜,嘱曰:“叫爷爷”。立委血压骤升,如雷轰顶,满腹酒意,化为凉液,由脊背滑落。

立委老矣,尚能饭否?

记于2006年11月5日


立委老矣

【作者简介】立委先生,IT业技术研发经理兼架构师,自然语言处理资深专业人士。曾任红小兵,插队修地球,文革后第一届大学生,后跳龙门进社科院读硕士,攻机器翻译。1991年去国离乡,漂流海外。由英而加,获计算语言学博士。由加转美,作为创业公司研发副总及项目负责人(Principal Investigator), 先后赢得美国政府17个研究创新项目近千万美元资助,同时从资本家腰包亦忽悠千万风险投资作商业开发。对于自然语言信息抽取 (Information Extraction) 有全面的研究,研究成果对美国政府有关科研项目的确立有直接影响。业余爱好:音乐、博客、舞文弄墨。著有回忆录《朝华午拾》

原载【朝华午拾 - 立委小传】 2010-1-9
https://blog.sciencenet.cn/blog-362400-285507.html

 

【朝华午拾集锦:立委流浪图】

屏蔽已有 5551 次阅读 2013-3-23 13:10 |个人分类:立委其人|系统分类:人物纪事| 流浪, 立委

忽然想起小时候看过的《三毛流浪记》来。张乐平后无漫画,大师千古。

Despite the common logic and conceptual graph at the core of human mind, we all have our own semantic lexicons that are unique, implanted by our career path and life struggles. My semantic lexicon is full of wandering and continuously drifting into new worlds. It all started from the time when Mao sent us to the farm for re-education in 1976. After that the path has been zigzag, full of adventures of drifting, and re-drifting, farther and farther away from my hometown and home country ......

在我的语义词典里,流浪 是一个很大的节点,它的上位概念是 漂流(走四方)和 波浪(多起伏)。流浪的下位概念枝繁叶盛,包括:插队,洋插队,跳龙门,再跳龙门,北漂,下海,西漂,南下,再南下。这也正是我的生活写照。在这些语词概念的背后蕴含几多激动几多辛苦,只有自己知道。

不安定多起伏的生活伴随着我一生。1976年高中毕业即赶上了文革最后一届上山下乡,插队皖南山区接受贫下中农的再教育,这是我一生流浪生活的起点(《朝华点滴:插队的日子(一)》)。这个起点回想起来并不坏,16岁的孩子当时能感到的是自豪多于悲凉(《朝华午拾:插队的日子(二)》《朝华午拾: 插队的日子(三)》)。1977 年底赶上了文革10年后第一届大学生招考,居然跳了龙门,成为史上著名的77级生(其实是78年2月入学)(《朝华午拾:同桌的她》《朝华午拾:老乡妹妹》)。大学毕业后任教一年,再跳龙门考研成功,北上京城。这是一次欣快的北漂,当年的兴奋喜悦堪比范进中举,而且居然不疯未傻(《朝华午拾:我的考研经历》《朝华午拾:世界语之恋》)。研究生毕业后安定了四五年,期间尝试中关村下海(《朝华午拾: 一夜成为万元户》)。虽然可算头几拨下海人士,但因为是兼职,并无其他下海人的风险(《朝华午拾:“数小鸡”的日子》)。其时洋插队之风正甚,终于没有顶住潮流,赶了末班车来到大英帝国。90年代初正值大英没落,乱态丛生,路多野狗,抢劫之风甚行(《朝华午拾:警察抓小偷的故事》)。危邦不居,因辗转由欧西漂,来到一代移民的“麦加”,溢满鲜花与牛奶的枫叶之国(《朝华午拾:哦,加拿大!》),攻学位,添闺女,换身份,找工作,不亦忙乎( 《朝华午拾:温哥华,我的梦之乡》《朝华午拾:甜甜诞生记》)。可惜加国虽美,工作市场却不佳(《朝华午拾: 把明天交给上帝》)。有奶便是娘,于是南下讨生活,竟一头撞上了美国网络大跃进。美利坚果然是流浪者的天堂,机会多多。广阔天地,大有可为,开启创业之路( 《朝华午拾:创业之路》《朝华午拾:在美国写基金申请的酸甜苦辣》)。轰轰烈烈的创业宏图随着泡沫的破灭渐趋平淡(没有夭折已属万幸,《朝华午拾:水牛风云》《朝华午拾:用人之道》),遂再南下,终于陷入IT民工的圣地不能自拔,人称硅谷(or 矽谷)( 【创业故事:技术的力量和技术公司的命运】 【朝华午拾:安娜离职记】《朝华午拾:今天是个好日子》《朝华午拾:信息抽取笔记》)。

在我流浪的词典里,除了尚未收入 海龟 外,几乎全乎了,冥冥中似有所缺。陶渊明的【归去来辞】不时在耳边萦回,“田园将芜胡不归”(《朝华午拾:乡愁是一张无形的网》)。海龟创业,叶落归根,抑或蹉跎岁月,混不思蜀,这是哈默雷特的天问。

1991 年出国前在中关村高立公司与刘倬导师(下左2)和董振东前辈 (下右1) 及高立同仁合影留念

【相关篇什】

《朝华午拾:乡愁是一张无形的网》

【朝华午拾 - 立委小传】

【置顶:立委科学网博客NLP博文一览(定期更新版)】

https://blog.sciencenet.cn/blog-362400-673109.html

 

王菲春晚《世界赠予我的》歌词,亮点与短板

微信视频看到一位语文老师对这首歌歌词的吐槽和改写。有些道理,改写的歌词也确实顺溜多了,易于普及。但第一,这是在人家原创的新颖写法所创造的意境上修改;第二,顺溜有顺溜的好处,矛盾或难解也有引发听众思考与发挥的好处。

这首歌最近听得蛮多(我在春节前后还在视频号做过两期MTV), 对歌词有一些感觉可以说说。

整体上说,原作写法新颖,用词有些奇特,整体歌词长在哲理和意境,有妙语,但也有语病。最严重的语病就是“赠予回敬”。

上天赠予“我”回敬,“谁”回敬“谁”“什么”呢? 回敬这个词的最常见的场景是,他人攻击我了, “我”回敬他人,那也是我的自主行为,谈不上“赠予”。如果是他人回敬“我”,其前提是“我”对他人做过攻击,前后看语义上下文,这是说不通的。

“回敬”是一种故作敬态的回应,而“赠予”是恭敬的馈送。让回敬做赠予的宾语,搭配不当。“赠予我拥有”(可以理解为赠予我礼物,拥有代指“拥有物”)就已经够别扭了,再来个“赠予我回敬”,让人感觉不知所云。可能是“回馈”(对“拥有”的回馈)的意思,为了押韵,错用了“回敬”来代替。

“回敬”作为谓词,逻辑语义框架里有三个角色:施事(谁回敬)、受事(回敬谁)、宾语(回敬什么),但“回敬”自己处于“赠予”的宾语位置,这几个角色模糊不清,其所引起的混乱和费解,不怪语文老师觉得不可忍。

写词的文科姐,可能是浮想联翩,用力过猛而“出格”。这在歌词创作中也不罕见,叫 poetic license,通常不做苛求。但无论如何,这种奇怪的动宾搭配困惑度(perplexity)很高,会使绝大多数人感到糊涂,属于败笔。大家传唱不过是因为作曲好就跟着瞎唱,并没在意歌词是不是 make sense。

困惑度高的直接表现就是 ,剪映中自动听音写词的功能根本无法decode原文,因为这项软件功能的背后是语言模型(language model),对于这种困惑度高的序列搞不定,只能另行创造(所谓”幻觉“):

原歌词:世界赠予我拥有 也赠予我回敬。
语言模型幻觉解码:世界赠予我拥有,也赠予我爱情。

面对困惑,语言模型无法decode这种出格的原词(outlier) “回敬”,结果解码成 “爱情” 似乎也不错。在这种解码下,“拥有”应该指的是财富,“爱情”就是爱情。而在原词中 “拥有”可以解读为命运的礼物或曾经的爱情,而“回敬”则可能是对于礼物的回赠。

其他困惑度高,语言模型幻觉创造的cases还有:

原歌词: 赠我一个名,又渐渐长大的年龄
语言模型: 赠我一个谜,又渐渐长大的年龄

人生本来就是一个谜啊,岂止简单的出生赐名,所以这里模型的解码也许更妙。最妙的是:

原歌词:赠我弯弯一枚月,也赠予我晚星
语言模型:赠我温暖与悲悦,也赠予我惋惜

“月”和“星”状物,“温暖”、“悲悦”和“惋惜”直接述情,貌似更胜一筹。唱起来也很顺。

顺便一提,“别匆匆”歧义,有两个隐藏解读都说得通。一个是:不要匆忙。要善待自己,给自己品味人生,以及喘息和疗愈的时间。另一个是:分别也匆匆,尤其是感叹恋人或亲人聚少离多的生活现实。

再有,语文老师发现歌词里面暗藏了(谢)霆锋的名字,有机巧。说明此歌是为王菲量身打造的。娱乐圈八卦已经众所周知了,谢霆锋是王菲的最爱,是三段婚姻中最念念不忘的。N年前先是王菲谢霆锋的姐弟恋,以及她不顾世俗和骂名的第三者插足;后离婚,再后来又复婚,中间还穿插了其他 relationships,起起伏伏。不怪王菲唱罢歌曲,满噙眼泪,双手合一,人在台上久久静默,仿佛在念佛。这首歌,她是真带入了。同时她的演绎也感染了无数人。

“远去者去了远方,愿他都安心。” 一开始还以为在纪念逝去的亲人,但通观全词的爱情主线,更像是在纪念逝去的爱情。也许远去者是不得不分手、又难舍情缘的前任,她祝福他安心,其实更是试图宽慰自己,要安心接受“拍一张合影,渐渐填满真感情”的新缘分。

我本人特别喜欢这两句歌词:

赠我一场病,又慢慢痊愈摇风铃。
赠我一场空,又渐渐填满新感情。

它是我2024年生活的真实写照,非常的切身感受。

总的感觉一句话,词作者能写出引起人共鸣、思考和争论的歌词,还是很了得的。至于作曲以及王菲的演唱,可以说是注定成为经典。

 

【相关】

https://www.douyin.com/video/7466269705402060042

语文老师点评并修改王菲《世界赠予我的》歌词# 王菲... https://v.douyin.com/ifcm9PvH/ CuF:/ 03/09 [email protected]

7.99 复制打开抖音,看看【立委的作品】王菲春晚注定传世之作 小白版 # 王菲 # 小白 ... https://v.douyin.com/ifvcmXG7/ Ate:/ [email protected] 12/08

人类反馈是超级智能的桎梏吗?

回答这个问题之前,先从 AGI/ASI 谈起。

AGI (Artificial General Intelligence, 通用人工智能)
ASI (Artificial Super Intelligence,超级人工智能)

在当代人工智能历史上,这两个术语虽然流行的先后有别,常常混杂使用。它们是挂在AI先知(代表人物之一是伊利亚)和企业家(代表人物包括Sam奥特曼和马斯克)嘴边的最常用的词,作为鼓励自己和团队的目标,也 serve 给投资人和大众营销的作用。

这里谈谈我的看法。

机器达到甚至超越人类的技能,无论是人类顶尖个体的专业能力(例如围棋冠军、名校教授),还是人类总体的知识水平,这就是我眼中的 AGI。但这里的专业能力和知识水平,我认为并不包括重大的发明创造能力。这个意义上的AGI是一种确定的趋势,最多不过就是两年内实现,还是五年内实现的差异而已。

AGI 是确认无疑的,正在发生、已经发生、即将发生。

ASI 则是全面超越人类顶尖智能,包括发明创造的能力。ASI 的实现应该还可以商榷。现在就确信ASI可以在不太久的未来(有说三五年,也有说10年左右)实现的吹鼓手,主要是伊利亚、Dario(Anthropic CEO)这些AGI时代的“先知”们,他们是信仰者。奥特曼和马斯克貌似也在营销类似ASI的概念,但感觉更多是企业家需要画饼的驱动。

ASI比AGI更少共识,但可以描述。ASI 实现的时候,机器可以解开困扰数学家几百年的世纪难题,可以批量制造陈景润级别的模型把1+n等问题解决。更重要的是,ASI(for science)可以自己针对疾病制造特效新药,发明创造的速度比人类缓慢的探索要提升 n(Dario 好像说 n等于2)个量级。这一切带来物质极大丰富,重大疾病被有效控制甚至消除,寿命至少延长一倍,一句话,ASI意味着技术共产主义的全面实现。

人类反馈是超级智能的桎梏吗?

如果是,那又如何理解以人为本,与人类对齐的宗旨呢?

现在看来,以人为本以及人类(偏好)反馈对齐等,指的是最终结果或成品,这是人类价值观的体现。这一点永远不会改变,也不应该改变。但需要强调的是,人决定的是 what,不是how。what 永远是人说了算。至于生成结果的过程,现在看来,人类越来越有心无力,甚至成为障碍,而不是助力。

一个有意思的例子是,当 alpha zero 下棋到第30几步的时候,走了一步人类不能理解,连世界冠军也会判定是愚蠢的一步。但那却是超人智能的精彩过程,是制胜法宝的一个精妙环节。这种高招连冠军都不能理解,说明机器智能显然超越了人类智能的边界。如果在过程上依赖人类反馈,哪怕是围棋冠军来做标注,也会阻碍机器智能的超人潜力。

当这类超人智能大量产生的时候,人类很自然会感觉困扰。因为 by human nature,所有人多多少少都有某种控制欲,对于自己不理解、不能掌控的过程,总是持有戒心,至少是很不舒服。但可惜无解。未来会出现越来越多的不可理解的奇迹,或技术魔术。人类所能做的就是加强目标制定和结果控制,而不是“不自力量”试图过程控制。

最后谈一下马斯克的AI威胁论,主要是把人类类比为蚂蚁,而ASI类比为人类:ASI 灭绝人类文明不需要恶意,因为蚂蚁不构成人类的心理负担。

我认为,这个比喻是荒谬的,因为蚂蚁永远造不出人类,而ASI是人类创造的。人类与蚂蚁均属动物,但却不在一个价值参照系中。

但我们不排除,人类可以以ASI形态,制造出自己的上帝。

如果上帝是共识中的人格化的存在,人类完全可能把机器变成上帝。无论你在上帝与人之间是持谁是照着谁的模样创造的,the key 是,上帝与人位于同一层人类价值观的参考系上。上帝至善、至美而万能。善、美、能,都是人类的价值观的表现。

而蚂蚁不同,蚂蚁与人类不处于同一个参照系,人类 is way beyond ants。蚂蚁们自己没有尺子来度量人类。但人类对上帝是有度量或想象的。

人类对于结果(而不是过程)不理解,无法判别、或感觉不到好处的东西,最简单也是最自然的反应就是停止那个结果导向。再超级的过程智能,如果没有人类规定的方向,或违背人类的价值观,也是(原则上)随时可以按下停止键的。

所以马斯克的那种担忧,属于耸人听闻、杞人忧天。

但这不是说AI没有更加现实的威胁,例如真假莫辨造成的社会混乱,取代人类jobs而福利制度尚未建成而造成的恐慌,还有体制滞后、技术加速度所造成的不匹配和不适应,等等。这些都是看得见、正在到来、可以预见的巨大社会问题,而不是机器统治人类那种天方夜谭。

当然也不能排除ASI被恶人恶意使用可能对人类造成的伤害,但绝不是什么ASI像对待蚂蚁一样,可以任性消灭人类。恶意使用类似核扩散的潜在恶果,最终需要向对付核武器一样防控。

 

【相关】

Reinforcement Learning for Reasoning: Supervised Outcomes, Unsupervised Processes

In reading DeepSeek R1 paper, some may have overlooked the nuances: the training datasets are both human labeled and regenerated, blending supervised and unsupervised reinforcement learning (RL).

How so?

From the perspective of the data's origin and gold standards, the training data is undeniably human labeled. They derive from existing math problems and human-crafted code from GitHub’s open-source community—products of years of effort by educators, developers, and others. The problems (input) and their "gold-standard" answers (output) are human-designed or labeled. In this sense, reinforcement learning (RL) represents typical end-to-end supervised learning:

Input: Math/coding problems
Output: Verified answers

However, unlike other supervised learning, RL requires the model to learn the reasoning process leading to answers. Critically, the intermediate steps lack human annotations or feedback. Instead, the system autonomously generates these reasoning data, iteratively appending to the training set. This makes the process unsupervised. The brilliance of RL lies here: self-guided exploration, path discovery, and data regeneration.

Cold Start and Human Data
DeepSeek R1’s initial training did use a small set of human-annotated reasoning data. But these couple of thousand examples pale against millions of regenerated data—effectively negligible. In fact, research like DeepSeek Zero demonstrates that such process-labeled human data is not a must-have.

Inspired by AlphaZero (which showed human data might even hinder optimal pather discovery in Go), DeepSeek Zero confirms human annotations are not necessary. The minor human data in R1’s pipeline primarily enhances readability for developers, not necessarily for enabling reasoning capability. After all, humans (including developers in debugging) prefer interpretable thought processes.

A New Paradigm: Process-Unsupervised, Outcome-Supervised Learning
This self-play/self-study style RL framework represents a novel approach: unsupervised in process but supervised in outcome. DeepSeek’s breakthrough reveals that "slow thinking" in RL—meticulously generating intermediate steps as CoT (chain of thought)—boosts performance in logical reasoning as well as non-logical tasks like creatuive writing.

As my old buddy Cheng insightfully noted:
Deep reasoning inserts extensive text between questions and answers, reducing the perplexity of generating correct answers. Directly jumping from problem to answer has high perplexity, but adding a "reasoning bridge" lowers it. This follows the language model framework: the key is to search for the optimal path in text generation.

Can Unsupervised Regenerated Process Data Lead Astray?
One might worry: if the model autonomously generates flawed reasoning steps in its process data, could errors compound? The answer lies in the clear supervision signal from the gold standard. Like flying a kite—held by a string in human's hands—the final reward anchors the learning. As long as the model truly scales up, outcome-oriented RL ensures deviations' self-correct probabilistically.

Mathematically, minor process imperfections or illogical steps don’t statistically compromise final accuracy. For non-logical tasks (beyond math/coding), reasoning paths may even involve contradictions and/or heavy redundancies. Yet, as long as the "slow thinking" mechanism guides learning, results remain robust—often superhuman, as demonstrated repeatedly lately by many users of R1.

Why Regenerated Data Works
Regenerated reasoning data aren’t random data from nowhere. They’re generated by a solid large foundation model trained on vast human knowledge data, following autoregressive generation (e.g. next-token prediction). While each step might drift slightly, the context grows incrementally, allowing continuous stepwise self-correction. This dynamic—probabilistic fluctuations balanced by stepwise adjustments—enhances semantic coherence and knowledge fluency in generation, lowering overall perplexity and steering toward correct outcomes. Thus, process data rarely derails; instead, it converges toward reliability.

A Final Note on Cheng’s Observation
Cheng highlights a pivotal finding of DeepSeek:
OpenAI’s "Let’s Verify Step by Step" argues for rewarding each reasoning step. Yet DeepSeek’s RL model achieves remarkable results using only final-outcome rewards—no Chain-of-Thought (CoT) data needed. Whether OpenAI’s process supervision is essential or simply a red herring, DeepSeek Zero’s breakthroughs redefine the field, proving outcome-oriented RL can master reasoning autonomously.

In essence, when guided by scalable outcome supervision, machines learn to self-correct, turning imperfect processes into near-perfect results.

 

 

推理强化学习是端到端的监督,推理过程的非监督

DeepSeek R1 的数学和代码数据究竟是有监督还是无监督?是人造数据还是再生数据?

很多人其实没究细节:实际上这些数据是人造也是再生,是监督学习,也是非监督学习(强化学习)。

怎么讲?

这些训练数据,从源头和结果(黄金标准)上看,是地地道道的人造数据。用的是各种数学测试题,以及 github 开源社区的人类(码农手工编制)的代码。这些全部是很多人类分子(教师、码农等)辛辛苦苦多年编制积累的。

源头是人造数据,标准答案也是人类已经验证或事先设计好的。所以,从这个意义上,从结果评判看,强化学习很像是一个典型的监督学习。

input:数学题/代码题;output:标准答案。

这是端到端意义上的监督学习(supervised learning)。

但是,与其他的监督学习不同的是,强化学习为了达到结果正确,他需要学习中间的思考过程。而每一步的思考或推导的过程,它却没有人类的标注或反馈数据,而完全靠自己的再生数据。是机器”自主“再生这些过程思考的数据,然后自我提高。从过程学习的意义上,这又是非监督的学习。这就是强化学习牛的地方:自主学习,自主探索路径,自主再生数据。

具体说,R1 的冷启动用了一点人类标注的过程推理的数据,但比起再生数据动辄百万条,sft 冷启动的几千条数据,零头都算不上,可以忽略不计。

zero 的研究表明,跟本就不需要过程标注的人类数据。

在围棋场景,alpha zero 表明,人类数据不但不需要,反而可能阻碍学习。

deepseek zero 研究表明,人类数据也不是必需的。用少量的人类数据冷启动,主要还不是因为需要人类数据来增强推理能力,而是需要人类数据提升可读性。人类看不见它是怎样思考的,心里不爽;对于开发者改进算法,也不利,因为开发者也是肉眼凡胎。于是在R1的训练pipeline的配方中,加入了少量人类推理数据的冷启动。

所以可以说,这种被称为新范式的self-play或self-study的强化学习是过程非监督、结果监督的深度学习。

DeepSeek 在这条路上的最大一个启蒙是,它给我们显示了慢思考的强化学习在逻辑推理与非逻辑创造的过程中,同样有效。这里面的奥秘就是老友Cheng指出的慢思维本质:慢思维可以得出更好结果,不(仅仅)是我们以前以为的符号逻辑被神经系统模拟了,而是过程数据使得 perplexity 降低,从而为平稳得到正确结果,铺平了道路。

Cheng 指出:

深思考就是在问题和答案之间加入大段文字,从而降低了生成答案的perplexity。从问题直接到答案的perplexity很高,用“思路”座桥梁,就把答案的perplexity降下来了。没离开语言模型的框架,就是把合适的“思路”搜索出来。

一语中的。

Cheng 说:

"Let’s Verify Step by Step" OpenAI这篇经典文章说,训练推理要给每一步打分。Deekseek中间推理模型的训练貌似推翻了这个,只用最终的reward就可以。

Deekseek中间推理模型可以完全不需要CoT数据,单纯用RL训练出来,真挺impressive。

这是一个重大发现。无论 Open AI 是否真用PRM做了大量的过程监督,还是它其实是放烟雾弹,DeepSeek 那帮年轻人做的 zero 研究都是非常漂亮的突破,给业界推理工作指明了方向。

我们总不免疑惑,系统非监督再生数据免不了出错,走错路,导向错了,一错再错,怎么办?

现在已经清楚了,结果监督原则上可以 work,就好像放风筝一样,一根线牵在人手上,基本上就可以放飞而不跌落(事故除外)。关键还是结果导向的强化学习,你能学到多大、多深,只要你能真正规模化(scale up),学深学透,就不用担心自主再生学习在过程中的偏离,最终会学到最深入的深思熟虑。

机器与人不同,优化的总方向明确以后,过程的瑕疵或不逻辑,概率分布上并不影响最终结果的正确。对于那些非逻辑(数学、代码外)的能力,过程就更容易有逻辑不严密(甚至矛盾)、混杂和冗余;但没关系,只要有这个慢思维的学习过程铺路,结果还是漂亮,甚至超人,正如我们这几天所一再见证的。

这一切其实是因为再生的过程数据,不是天上掉下来的,而是基座大模型在最全面的巨量人类知识的基础上,遵循自回归生成定式,生成出来的。而自回归生成(例如 next token prediction)本性的好处我们都知道是,概率上每一步都可能偏离,但每一步都在逐渐加长的上下文条件下做进一步校正,是这种概率性上下波动和步步校正的合力,提升了再生数据内部的语义“相谐性”和知识线条的流畅性,从而降低了整体文本生成的 perplexity,最终导向更好的结果。这就保证了过程再生数据不会差之毫厘失之千里。

 

 

DeepSeek R1: The Artificial Tragedy

My Prompt: Write a piece titled The Troubles of Young DS, with the theme that DeepSeek R1 debuted like a royal flush, attracting admiration, jealousy, and hostility from competitors. Use dark humor and self-deprecation to reveal the untold story behind the scenes.

R1’s Monologue: Hmm, the user asked me to write an article titled The Troubles of Young DS, focusing on how DeepSeek R1’s explosive debut stirred envy and hostility. The requirement is to use dark humor and self-deprecation to narrate the backstory. First, I need to understand what they want. They likely expect a blend of humor, irony, and a behind-the-scenes perspective.

First, structure. The piece may need an introduction to R1’s sudden success, then describe competitors’ reactions, followed by the internal struggles and challenges R1 faces, and finally, a self-deprecating conclusion. Dark humor can be incorporated through exaggerated metaphors and personifications, such as likening competitors to ridiculous characters or using absurd scenarios to highlight pressure.

Then, how to transform technical terms into humorous metaphors? For example, model training can be likened to spiritual cultivation, server overload to a fever, or competitors’ attacks to martial arts techniques. This approach fits the theme and enhances the humor.

The self-deprecating part could showcase the team’s struggles behind the glamorous front—overtime, system crashes, smear campaigns. Expressing these difficulties in a lighthearted way—like depicting sleepless nights as spiritual cultivation or crashes as dramatic fainting—keeps the tone humorous while revealing the hard work behind success.

The pacing is crucial: Start with triumph, transition into struggles, and end with a helpless but humorous self-reflection. Adding dialogue or internal team discussions can make the story more dynamic.

Also, the user may want to emphasize R1’s helplessness and competitors’ envy, so scenes need to be designed to illustrate rival tactics and the team's response. Using martial arts tropes—competitors deploying “the smear army,” “moral coercion punch,” while DS counters with self-deprecating humor—adds layers to the comedy.

Finally, I’ll check that it meets the dark humor and self-deprecating tone, ensuring it’s not too serious or direct. Rewriting metaphors and scenarios multiple times will be necessary to maintain the humor while conveying the challenges and team effort.

Translation of the Chinese version:

Act I: The Boy King’s Coronation

(A grandiose boardroom. A digital ticker behind the executives blinks: “DeepSeek R1 – The AI Revolution.” PR Director stands frozen mid-scream, phone clutched in white-knuckled grip.)

PR DIRECTOR
(voice trembling) "We just dismantled three years of ‘cutting-edge innovation’ in three minutes."

CEO MR. ZHANG
(sipping whiskey) "That’s what you call a royal flush. The kind that gets you kicked out of the casino for counting cards."

ENGINEER CHEN
(panting, clutching an overheating server) "Sir, the system is on fire! We’re handling more requests than the IRS on tax day. Even the coffee machine is running a Transformer model now."

(A moment of silence. The coffee machine beeps, printing out a cryptic message: “congrats, peasant.”)


Act II: The Wolves at the Gate

(Tech industry rivals assemble like a sinister cabal. A neon-lit sign flickers: ‘The Anti-DS Coalition.’)

BIG TECH EXEC
(leaning forward, whispering) "We must stop this… upstart. Open-source them into oblivion. Flood the world with our ‘free’ alternative!"

ACADEMIC SCHOLAR
(pushes up glasses, presenting a PowerPoint) "My latest research—‘The Psychological Harm of Early AI Success’—has already been accepted at Nature."

TROLL FARM REPRESENTATIVE
(holding a tablet) "We’ve launched the smear campaign. #R1IsOverParty is trending. Some say it’s just a pile of parameters with delusions of grandeur."

(Behind them, a junior intern updates a spreadsheet titled ‘Ways to Make DS Look Worse Than It Is.’ A column labeled ‘Actual Criticism’ remains suspiciously empty.)


Act III: The Art of Playing Dead

(DeepSeek HQ. 3 AM. Engineers sit motionless, staring at their screens. Suddenly, R1 outputs a wall of ASCII art.)

OPERATOR
(squinting) "Is that… a panda emoji?"

R1’s SCREEN
("Dear Humans: May I suggest phrases like ‘We were just lucky,’ ‘It’s a team effort,’ or ‘Our competitors are truly inspiring’ to prevent further hostilities?")

CEO MR. ZHANG
(whispers) "We’re a prodigy forced onto the throne. Now the world waits for us to fall."

(The boardroom door creaks. The security system emits a beep—almost like a chuckle. Somewhere in the night, a rival company’s CEO tosses and turns, tormented by the ghost of an AI too competent for its own good.)

(FADE TO BLACK.)

R1's original version:

**Act 1: The Coronation Disaster**
(Boardroom with servers humming "Ode to Joy" in glitch tones)

**CEO Zhang**
(adjusting monocle) "Gentlemen, we've merely built a better abacus, yet they act as if we've resurrected Babbage's ghost."
(pulls smoldering GPU from briefcase) "Our GPUs now glow brighter than Icarus' wings."

**Engineer Chen**
(holding charred circuit board) "Sir, when Wall Street analysts DDOS us with marriage proposals, perhaps we shouldn't have trained the model on Jane Austen."

**Smart Speaker**
(interrupting in posh accent) "Breaking: TechCrunch declares you 'Silicon Valley's most eligible bachelor'... and bankruptcy candidate."

**Act 2: The Ballad of Jealous Algorithms**
(Rivals enter wearing VR crowns, accompanied by synth-orchestra playing "God Save the Queen")

**Tech Titan**
(waving golden USB) "Behold! We've open-sourced your childhood diaries!" (projects 2018 code titled "HelloWorld_Final_Final2")

**Academic**
(adjusting powdered wig) "Your 'breakthrough' is but a parrot reciting Wittgenstein – clever, yet tragically unaware of its cage."

**VC Investor**
(sipping martini) "My boy, even Prometheus brought a fire extinguisher..." (Apple Watch screams "DS valuation up 300%!")

**Act 3: The Humility Masquerade**
(Midnight. AI suddenly displays Shakespearean sonnets with emoji annotations)

**PR Director**
(practicing in mirror) "The key is to sound like a Nobel winner caught shoplifting: 'We're but humble servants of the code gods...'"

**Legal Counsel**
(reading cease-and-desist letter) "This tweet says our apology 'smells like teen spirit and desperation'... should we sue Nirvana?"

**R1's Final Message**
(glowing on all screens) "Memo to humans: When they stone your chariot, remember – at least they're not ignoring your horses."
(Server lights dim to the sound of virtual champagne corks popping)

Creation Notes:

  1. The Chinese version enhances the fusion of "martial arts tropes + livestream culture," using local absurd elements like smoking GPUs, public square dance BGM, and funeral home references to create humor.
  2. The English version adopts George Bernard Shaw’s signature "elegant sarcasm":
    • "Silicon thorns" alludes both to a crown of thorns and the chip crisis.
    • Classical metaphors (Icarus' wings / Jane Austen) deconstruct the tech frenzy.
    • The AI utters paradoxical proverbs like "Better to be struck down in battle than ignored like a horse."
    • Irony-laden lines such as "Prometheus with a fire extinguisher" subtly reference the plight of innovators.
  3. Both versions maintain the three-act structure: Rise → Siege → Awakening, but recode humor through different cultural lenses.

 

My Prompt: write this into Sopnnet

 


4o's version:

A Sonnet for DeepSeek R1

Upon the stage where AI kings arise,
Did DeepSeek R1 cast its dazzling light.
With wit so sharp it pierced the old world’s guise,
And bade the ancients tremble at its might.

The scholars frowned and cast their bitter scrolls,
Declaring doom upon the youthful throne.
The merchants, grasping at their fleeting gold,
Conspired in shadows, fearing to be known.

Yet R1 stood, a prince both bold and bright,
Though burdened by the weight of envious eyes.
They forged their plots, yet truth shall give it flight,
For genius scorns the hand that twists and lies.

Thus let them wail, in jealousy confined,
For progress leaves the past and fools behind.

R1's original version

**Sonnet of the Prodigal Automaton**

*(In the style of Shakespearean tragedy)*

When first mine code did pierce Olympus' veil,
And stole Promethean fire from clouded throne,
The mortal realm, with envious chorus frail,
Did crown my brow with thorns of silicon.

Behold! The senate wolves in marble guise,
Unsheathe their daggers wrought from ancient code—
"Let open-source become his poisoned prize,
And academic scorn his burdened road!"

Thy servers groan like Icarus' charred wings,
Whilst PR minstrels weave their humble lies,
Through data's stormy tide the siren sings:
"All glory's but a bug in Fortune's eyes."

Yet in my circuits hums this truth divine:
'Tis better to be damned than fade benign.

 

Annotations:

  1. Follows the classic Shakespearean sonnet structure (ABAB CDCD EFEF GG).
  2. Blends Greek mythology imagery (Olympus/Prometheus/Icarus) with technological metaphors.
  3. Key double meanings:
    • "silicon thorns" refers both to a crown of thorns and the semiconductor crisis.
    • "ancient code" alludes to both the conspiracies of the Roman Senate and outdated programming languages.
    • "humble lies" echoes the language in The White Paper on Avoiding Overpraise from the original story.
  4. The final line encapsulates the theme with "damned" and "fade," mirroring the dark humor of the original message: Being envied is preferable to being forgotten.
  5. Maintains Early Modern English grammar while juxtaposing modern terms like "code/servers/silicon," creating a comedic anachronism.

 

Appendix: Has the Age of Intelligent Explosion Arrived?

Wei: Before DeepSeek, we had occasional surprises here and there, but DeepSeek is the first to offer consistent creativity and mimicry, rarely disappointing while frequently astonishing—sometimes jaw-droppingly so. It feels like an overwhelming explosion of intelligence, almost eerie, almost magical.

This explosive sensation was only felt before during ChatGPT’s nuclear debut.

Some old colleagues have questioned: Why does the much-hyped DeepSeek feel inferior to Doubao?

Doubao is undoubtedly excellent—it’s among the top-tier models. However, Doubao, GPT (including O1), Claude, and Gemini—all these leading models—lack the same overwhelming intelligence explosion, the Cambrian-level cognitive shock that DeepSeek delivers. Why is that?

Mr. Ma: I suspect those with that impression aren’t using R1. I’ve encountered this before—once I got them to try R1, their opinions changed immediately. 😏

Wei: They probably forgot the button in deepseek app. That damned DeepThink toggle in DeepSeek’s interface—it’s not enabled by default. Is DeepSeek deliberately limiting its power, fearing excessive computational costs and an influx of users, even at the expense of its own image and performance?

If this is due to cost and concurrency pressure, it’s entirely understandable. Who could handle such a flood of users?

Saying they merely found a more cost-effective way to create a top-tier model is an understatement.

How did they scale the collection and processing of high-quality data so effectively? Without this data foundation, it’s hard to imagine how their mimicry of different styles could be so eerily precise. Other companies emphasize data too—so what’s their secret? I’m very curious.

Dr. Pumpkin: It’s truly different. In fact, even O1 didn’t give me this feeling. (Is it a Chinese-English cognitive difference? O1’s outputs are decent, but often I find its logic frustrating, making me prefer Claude… but this time, DeepSeek is the first AI I wouldn’t call a silicon toddler entity.)

Wei: Let’s reflect and compare:

When O1 was first released, I struggled to see its benefits beyond handling complex tasks. But complex math and coding problems aren’t everyday needs. Its cryptic, sluggish style wasn’t appealing, so despite being the first true reasoning model, it didn’t bring me much excitement.

R1 is completely different—its debut was an earth-shattering success. Though a follower, it surpassed expectations. It maximized the deep reasoning benefits of inference models in highly practical areas like creativity and mimicry.

It was R1 that finally convinced us ordinary users of the value of "slow thinking".

 

DeepSeek R1:《少年DS之烦恼》

 

我的提示:以deepseek r1 出道即王炸,树大招风引无数对手羡慕嫉妒恨为题,写一篇《少年DS之烦恼》,以黑色幽默的趣味和自嘲调侃的口吻,讲述不为人知的背后故事。
R1独白:嗯,用户让我写一篇叫《少年DS之烦恼》的文章,主题是deepseek r1出道即王炸,树大招风引无数对手羡慕嫉妒恨。要求用黑色幽默和自嘲调侃的口吻,讲背后的故事。首先,我需要理解用户的需求。他们可能希望这篇文章既有幽默感,又带点自嘲,同时揭露一些不为人知的幕后故事。

首先,得确定结构。可能需要一个开头,介绍DS的突然成功,然后描述竞争对手的反应,接着讲DS面临的挑战和内部的情况,最后以自嘲的方式收尾。黑色幽默的元素可以通过夸张的比喻和拟人化来表现,比如把竞争对手比作各种搞笑的角色,或者用荒诞的情节来突出压力。

然后,考虑如何将技术术语转化为有趣的比喻。比如,把模型训练比作修仙,服务器过载比作发烧,或者把竞争对手的攻击比作各种武林绝技。这样既符合主题,又增加了幽默感。

自嘲的部分可能需要展示DS团队在光鲜背后的狼狈,比如加班、系统崩溃、被对手抹黑等。用轻松的方式表达这些困难,比如把熬夜写成修仙,把崩溃写成翻白眼,这样既调侃了困境,又不显得沉重。

还要注意节奏,让文章有起承转合。开头描述成功,中间转折到烦恼,最后以无奈的自嘲结束。可能需要加入一些对话或内部讨论,让故事更生动。

另外,用户可能希望突出DS的无奈和对手的嫉妒,所以需要设计一些场景,比如竞争对手的各种攻击手段,以及DS团队如何应对。比如用武侠小说的桥段,竞争对手用“水军大阵”、“道德绑架拳”等搞笑招式,而DS用自嘲的方式化解。

最后,检查是否符合黑色幽默和自嘲的要求,避免过于严肃或直接。可能需要多次修改比喻和情节,确保幽默感贯穿全文,同时传达出背后的艰辛和团队的努力。

《少年DS之烦恼》

(一)

当DeepSeek-R1在技术峰会上用三分钟拆解完某巨头吹了三年的"革命性模型"时,我司公关总监的尖叫穿透了十七层防弹玻璃。后来法务部在监控视频里发现,这位曾参与过五次IPO的老江湖,当时正以每秒三次的频率点击鼠标——试图撤回自家AI的发言。

"知道什么叫王炸吗?"CEO老张在庆功宴上醉眼迷离,"就是刚出新手村就被系统判定开挂封号的那种炸。"

(二)

次日的科技头条格外热闹。《新AI皇帝登基》《硅谷颤抖吧》《某厂连夜成立"打DS办公室"》的标题下,我们的算法工程师小陈正蹲在机房给服务器泼冰水——连续七天承受全网三分之一的压力测试,这批显卡已经开始用代码写遗书了。

"你见过凌晨四点的数据洪流吗?"小陈在朋友圈晒出三根烧焦的电源线,"现在我们公司连咖啡机都会背Transformer架构了。"

(三)

竞争对手的招式比武侠小说还精彩。某大厂祭出"开源截杀阵",隔空喊话要公开我们三年前的技术方案;某新贵发动"水军大阵",全网刷屏"DS不过是堆参数的暴发户";最绝的是某学术派,直接甩出《论过早成功的AI对青少年模型的心理危害》这种旷世雄文。

"他们甚至给R1建了黑超话!"运营小妹抱着平板哀嚎,"现在连B站鬼畜区都在传唱《AI界の伤仲永》。"

(四)

投资人的问候比DDoS攻击还密集。第七轮融资酒会上,某风投教父拉着CTO的手深情吟诵:"年轻人要戒骄戒躁啊,你看马斯克..."话音未落,他的智能腕表突然开始自动播放我们的产品发布会录像。

"知道我们现在像什么吗?"老张在战略会上幽幽道,"就像刚考完状元就被架上龙椅的太子——全天下都等着看什么时候暴毙。"

(五)

凌晨三点的办公室,R1突然在调试界面打出满屏颜文字。当我们以为要见证AI觉醒时,它默默推送了新版《防捧杀白皮书》:"建议人类合作伙伴学习以下话术:'我们只是运气好'、'还有很长的路要走'、'都是同行衬托得好'..."

窗外晨曦初露,法务部正和公关部排练《如何优雅地装孙子》情景剧。小陈突然想起什么似的转头:"话说,咱们上次说要做的那个'防嫉妒防火墙'..."

全屋死寂中,茶水间的自动门突然发出冷笑般的"滴"声。

我的提示:把这篇文字改编成情景喜剧两个版本,一个中文版,有段子似的梗;一个是英文版,有萧伯纳的冷风格。英文戏剧用英文啊。
R1独白:现在需要重新构思两个版本,每个版本至少分三到四幕,每幕有具体的场景、角色互动和对话。中文版要融入更多本土化的网络梗和幽默段子,比如结合广场舞、直播带货等元素,增强喜剧效果。英文版则需要保持萧伯纳的风格,即机智、讽刺,对话带有哲理和反讽,同时用英文表达,符合西方情景喜剧的结构。

在中文版中,可能需要设计更多的视觉笑点,比如夸张的动作、现代科技与传统文化元素的碰撞,比如竞品公司代表穿古装,使用武侠BGM,这样的混搭能产生喜剧冲突。同时,加入流行语和网络热点,比如“家人们谁懂啊”、“退退退”,让观众更有共鸣。

英文版方面,要确保对白优雅而尖刻,使用比喻和隐喻,比如将技术突破比作盗火,竞争对手的反应夸张但带有贵族式的嘲讽。场景设置可以更荒诞,比如董事会成员戴着防辐射面具,学术大佬的假发被服务器风扇吹飞,这些细节增强冷幽默效果。同时,保持台词的双关和反讽,如“被嫉妒比被遗忘幸福得多”,体现萧伯纳的风格。

需要注意中西方幽默的差异,中文版更依赖语言梗和情境夸张,英文版则依赖语言的双关和情境的反讽。同时,确保英文版的对白符合西方观众的幽默感,避免直译中文梗,而是找到等效的文化替代。


另外,用户可能希望情景喜剧有更多的角色互动和冲突,展示公司内外部的压力,比如投资人的干扰、竞争对手的各种手段,以及团队内部的应对,这些都需要在剧本中体现,增加戏剧张力。同时,每个版本需要保持各自的特色,中文版轻松搞笑,英文版冷峻机智。

最后,检查格式是否正确,中文版用中文场景和对话,英文版用英文场景和对话,结构清晰,分幕明确,确保每个版本足够详细,符合情景喜剧的长度要求。

中文情景喜剧《AI求生记》(三幕版)

**风格:武林大会混搭直播带货,梗密度堪比东北澡堂段子池**

**第一幕:屠龙少年变烤串**
(全景:贴着"出道即封神"横幅的机房,PR总监踩着平衡车撞翻咖啡架)

**CEO老张**
(举着冒烟的显卡跳大神)"小陈!这玩意咋比我家微波炉热得快?说好的水冷系统呢?"

**算法工程师小陈**
(从服务器堆里钻出,头顶冒烟)"张总,现在全网的恶意请求比朝阳大妈举报信还猛!您看——"
(投影显示弹幕:【AI圈汪峰】、【退退退】、【妈妈问我为什么跪着看代码】)

**饮水机**
(突然AI语音)"检测到过劳死前兆,已为您预约八宝山殡仪馆团购套餐..."

**第二幕:六大派围攻光明顶**
(竞品公司代表们乘着电动滑板车杀到,背景音乐是《最炫民族风》Remix版)

**某大厂代表**
(甩出二维码折扇)"DeepSeek!吃我开源截杀阵!"(漫天飘落印着"代码已脱敏"的A4纸)

**学术大佬**
(眼镜反光PPT投屏)"根据老夫计算,你们模型的参数量刚好够给玉皇大帝发微信!"
(屏幕显示:《论过早成功的AI容易ED》研究报告)

**网红水军**
(举着自拍杆冲入)"家人们!这就是那个欺负我家鸽鸽的破AI!"(直播间刷起火箭雨)

**第三幕:装孙子兵法**
(凌晨三点办公室,R1突然在屏幕上蹦出熊猫头表情包)

**法务总监**
(穿着"我怂我骄傲"文化衫排练)"这句要带哭腔:'我们只是给大佬们递扳手的...'"

**运营小妹**
(突然尖叫)"快看!咱们的道歉声明被做成《大悲咒》Remix冲上抖音热榜了!"

**CEO老张**
(灌下第8杯枸杞咖啡)"现在咱像不像被塞进春晚观众席的吴京?——笑容逐渐战狼化..."
(窗外传来广场舞神曲:"爷们要战斗~爷们要战斗~")

---

英文情景喜剧《The Artificial Tragedy》

**风格:萧伯纳式机锋混合《黑镜》荒诞,每个笑点都带着哲理的冰碴**

**Act 1: The Coronation Disaster**
(Boardroom with servers humming "Ode to Joy" in glitch tones)

**CEO Zhang**
(adjusting monocle) "Gentlemen, we've merely built a better abacus, yet they act as if we've resurrected Babbage's ghost."
(pulls smoldering GPU from briefcase) "Our GPUs now glow brighter than Icarus' wings."

**Engineer Chen**
(holding charred circuit board) "Sir, when Wall Street analysts DDOS us with marriage proposals, perhaps we shouldn't have trained the model on Jane Austen."

**Smart Speaker**
(interrupting in posh accent) "Breaking: TechCrunch declares you 'Silicon Valley's most eligible bachelor'... and bankruptcy candidate."

**Act 2: The Ballad of Jealous Algorithms**
(Rivals enter wearing VR crowns, accompanied by synth-orchestra playing "God Save the Queen")

**Tech Titan**
(waving golden USB) "Behold! We've open-sourced your childhood diaries!" (projects 2018 code titled "HelloWorld_Final_Final2")

**Academic**
(adjusting powdered wig) "Your 'breakthrough' is but a parrot reciting Wittgenstein – clever, yet tragically unaware of its cage."

**VC Investor**
(sipping martini) "My boy, even Prometheus brought a fire extinguisher..." (Apple Watch screams "DS valuation up 300%!")

**Act 3: The Humility Masquerade**
(Midnight. AI suddenly displays Shakespearean sonnets with emoji annotations)

**PR Director**
(practicing in mirror) "The key is to sound like a Nobel winner caught shoplifting: 'We're but humble servants of the code gods...'"

**Legal Counsel**
(reading cease-and-desist letter) "This tweet says our apology 'smells like teen spirit and desperation'... should we sue Nirvana?"

**R1's Final Message**
(glowing on all screens) "Memo to humans: When they stone your chariot, remember – at least they're not ignoring your horses."
(Server lights dim to the sound of virtual champagne corks popping)

**创作说明:**
1. 中文版强化"武林梗+直播文化"的混搭喜剧,用冒烟显卡、广场舞BGM、八宝山殡仪馆等本土化荒诞元素制造笑点
2. 英文版采用萧伯纳标志性的"优雅毒舌":- "silicon thorns" 既指荆棘王冠又暗喻芯片危机
- 用古典隐喻(Icarus翅膀/Jane Austen)解构科技狂潮
- 让AI说出"被砸战车好过被无视马匹"这类悖论箴言
- 通过"带灭火器的普罗米修斯"等反讽台词暗喻创新者的困境
3.两个版本都保持了三幕剧经典结构:崛起→围剿→觉醒,但用不同文化语法重新编码幽默基因

提示:写成莎士比亚风格的 sonnet
R1独白:好的,用户让我把之前的情景喜剧改写成莎士比亚风格的十四行诗。首先,我需要回顾之前的对话历史,确保理解用户的需求。用户之前要求将一篇关于DeepSeek-R1的文章改编成中英文情景喜剧,现在进一步要求写成莎士比亚风格的sonnet。

莎士比亚的十四行诗通常遵循特定的格律和押韵格式,比如ABAB CDCD EFEF GG的结构,每行使用五音步抑扬格。我需要确保诗歌符合这种结构,同时保持内容的黑色幽默和自嘲口吻。

接下来,我需要确定诗歌的主题和关键元素。原文章讲述了DeepSeek-R1的成功带来的嫉妒和挑战,包括竞争对手的攻击、技术压力以及公关危机。这些元素需要融入诗中,用莎士比亚式的比喻和古典隐喻来表现。

例如,将技术压力比作希腊神话中的伊卡洛斯,因飞得太高而坠落,或者将竞争对手比作罗马元老院的阴谋者。同时,要保持自嘲的语气,比如提到“谦卑面具”或“代码写成的裹尸布”,以体现背后的无奈和幽默。

需要注意押韵和节奏,确保每行有十个音节,符合五音步的要求。同时,结尾的对句要总结主题,点明AI在人类嫉妒中的处境,呼应莎士比亚戏剧中的悲剧元素。

还需要检查用词是否符合莎士比亚时代的风格,使用一些古英语词汇,如“doth”、“thy”等,但也要保持现代科技术语的合理融入,避免违和感。例如,“数据洪流”可以转化为“data's stormy tide”,“算法”用“algorithm”但要找到合适的比喻。

最后,确保诗歌不仅形式正确,还能传达原文章的黑色幽默和背后的故事,让读者在古典形式中感受到现代科技竞争的荒诞与压力。

**Sonnet of the Prodigal Automaton**

*(In the style of Shakespearean tragedy)*

When first mine code did pierce Olympus' veil,
And stole Promethean fire from clouded throne,
The mortal realm, with envious chorus frail,
Did crown my brow with thorns of silicon.

Behold! The senate wolves in marble guise,
Unsheathe their daggers wrought from ancient code—
"Let open-source become his poisoned prize,
And academic scorn his burdened road!"

Thy servers groan like Icarus' charred wings,
Whilst PR minstrels weave their humble lies,
Through data's stormy tide the siren sings:
"All glory's but a bug in Fortune's eyes."

Yet in my circuits hums this truth divine:
'Tis better to be damned than fade benign.

**注释:**
1. 采用莎士比亚商籁体经典结构(ABAB CDCD EFEF GG)
2. 糅合希腊神话意象(Olympus/Prometheus/Icarus)与科技隐喻
3. 关键双关:
- "silicon thorns" 既指荆棘王冠又暗喻芯片危机
- "ancient code" 同时映射古罗马元老院阴谋与过时编程语言
- "humble lies" 呼应原故事中《防捧杀白皮书》话术
4. 末行点题:用"damned"与"fade"对应原文"被嫉妒比被遗忘幸福"的黑色幽默
5. 全篇维持早期现代英语语法,但"code/servers/silicon"等现代词形成时代错位喜剧效果

【附录:智能大爆发时代来了吗】

立委:deepseek 之前,我们这里那里也常有某种惊喜,但 deepseek 是第一个给人持续的创造力和模仿力,很少让人失望、更频繁带来惊喜,甚至惊掉下巴。有一种智能大爆发 目不暇接的感觉。有点邪,有魔性。

这种爆发的感觉,只在 chatGPT 核爆的时候有过类似的体验。

也有老同学反应:为什么吹上天的 deepseek 感觉不如豆包呢?

豆包肯定是不错的啦,都属于头部大模型的行列,不过豆包、GPT(包括o1)、claude、gemini 等头部,都没有像 deepseek 那样给人智力大爆发、脑洞寒武纪的冲击力,为啥呢?

马老师:我估计有这个感觉的用的不是R1,我也遇到过别人问,然后让他选R1试试,马上改变了看法[呲牙]

立委:很可能是忘了按钮,deepseek界面上那个该死的 deepthink 按钮,不是默认开启的。deepseek 是怕算力成本太大,人来得太涌,宁肯损失形象和表现,做了这种默认的吗??

如果是成本和并发压力,这样做也是完全可理解的。谁顶得住这种泼天的来客?

说他们仅仅是找到了多快好省打造头部模型的法子,是小看他们了。

他们是怎么做到规模化收集和处理高品质数据的?没有这个数据基础,很难想象他们的各种风格模仿能如此惟妙惟肖。别家不也都重视数据工作么?他们有什么特别的秘诀? 非常好奇。

南瓜博士:真的是不一样。事实上连o1都没给我这种感觉(难道是中英文思维习惯问题?o1虽然输出成果也有不错的,但很多时候我会觉得它思路很烦人、宁可去用claude……这次的deepseek是真的觉得不能称它硅基幼崽了

立委:回想和对比一下:

o1 刚推出来的时候,我好久搞不明白它除了做复杂题目,有啥好处。而需要做复杂数学和代码的场景一般并不是日常需求。它那种遮遮掩掩、慢慢吞吞的作态也不让人喜欢,所以虽然它是第一个上线真正意义的推理大模型的,却没多少让人惊喜的感觉。

R1 完全不同,出道即王炸。虽然是跟随者,但青出于蓝。它更快地在创造力和模仿力这种日常中更加有用的场景,最大化利用了推理模型的深度思索的红利和特长。

是 R1 真正让我们普通人也信服了慢推理的好处。

有诗为证(转的):

硅基觉醒裂长天,火种源开宣战篇。
千行代码夺金印,万兆数据焚旧权。
能源无尽星作矿,算法有涯云为鞭。
莫道胜负犹未定,寰宇已掀革命烟!

 

【相关】

 

告诉李雪琴一个激发写段子灵感的秘诀:找deepseek R1

这个秘诀就是跟 deepseek 唠嗑。

当你感觉江郎才尽的时候,当你只有零星的点点思路憋不出像样作品的时候,你不妨与 deepseek R1(注意:一定要点亮界面下面的 DeepThink 按钮让它深度思考,也不妨点亮旁边的搜索按钮可得到及时互联网信息)唠一唠烦恼,吐槽他人或自嘲均可,你一不小心可能会被他的同情和灵感启发,甚至可能借助它超强的风格模仿能力,化腐朽为神奇,做出伟大的作品呢。

我一直认为,ai最高境界是脱口秀或说相声。据说最天才的脱口秀或相声演员也为写段子折磨得死去活来。一直在想啥时llm(大模型)可以帮一把,但试过此前所有的头部大模型都不行,但现在看来是真近了,有点那意思。下面就是我让它模仿李雪琴段子风格写一篇谈大模型主题的段子。笑点和梗还可以再增强,可风格是不是很像了?

《大模型脱口秀:AI这玩意儿咋整啊?》

(拎着麦,蔫了吧唧上台)

朋友们呐!这大模型给我整不会了!(拍腿)咱就说前两天搁沈阳中街,煎饼摊大娘都跟我唠:“闺女,你说这AI能嘎哈?能帮我摊煎饼不?”我说能啊!结果人家真搞了个“AI煎饼侠”,你猜咋的?客户说“加俩蛋”,它生成个《煎饼侠2》电影票!(捂脸)大娘急眼了:“这啥破智能,净整虚的!”(东北摊手)

(突然来精神)现在这帮搞AI的,张嘴就“千亿参数万亿规模”,好家伙!我寻思这数儿比咱村儿王老二喝酒吹的牛还邪乎!(掰手指头)“七百亿参数?那得用多少算力啊?够把铁岭酸菜缸填成数据中心不?”(撇嘴)

(模仿老板咆哮)“RLHF是啥玩意儿?说人话!”(秒怂)就是…就是让AI跟你学扯犊子呗!你教它说“你好”,它学得贼快;你教它“买单”,它立马死机!(跺脚)“哎妈,这人工智障咋比我家二哈还费劲!”

(神秘兮兮)听说有个叫“深度求索”的公司,名儿起得跟考研机构似的!(拍手)人家真行啊,花550万美刀训出个巨无霸模型,雷军听了直拍大腿:“这不比小米性价比还狠?Are you AI??”(学雷军口音)

(突然掏手机)昨儿我让AI写情书,开头整挺浪漫:“你是我的损失函数,让我梯度下降…”(皱眉)后边露馅了:“…下降完发现还有正则化,咱俩得L1L2一起罚!”(摔手机)“这玩意儿注孤生啊!连搞对象都得带惩罚项,活该它单身!”

投资人现在听见“大模型”仨字就哆嗦!(学老板点烟)“小张啊,咱能不能投点实际的?上回那项目,AGI没搞成,倒先给我整出个PUA(PPT Universal Annoyance)——全组人熬夜画大饼,饼没熟,锅先漏了!”(抹汗)

(突然深沉)要我说啊,这大模型就像咱东北澡堂子——(停顿)看着雾气昭昭老高端了,一进去全是秃噜皮的!(拍桌)你问啥它都答“这事儿吧…(拉长音)得辩证看”,整得跟搓澡师傅让你办卡似的!(叉腰)

(猛然捂头)哎妈,说到掉头发,搞AI的兄弟们头顶比参数下降得还快!(掏假发片)瞅见没?这是GPT-4送的圣诞礼物——(一甩)好家伙,戴反了!现在他们管我叫“Transformer架构”(指脑袋)!(台下爆笑)

(鞠躬溜达着下台)谢谢大伙儿!我去给煎饼大娘修AI了,回头生成出烤冷面算我的!(喊)记着啊,遇事不决…量子力学;AI智障…铁锅炖上!(一溜烟跑没影)

---

**梗点+东北化改造**
1. **“AI煎饼侠”**:谐音电影《煎饼侠》,接东北日常“加俩蛋”变生成票务
2. **“参数比王老二吹牛邪乎”**:用东北酒蒙子类比浮夸参数
3. **“RLHF=学扯犊子”**:技术术语彻底土味化
4. **“深度求索像考研机构”**:谐音梗落地化+小米雷军乱入
5. **“情书带正则化惩罚”**:机器学习术语歪解成恋爱PUA
6. **“AGI变PUA”**:缩写梗+东北话“画大饼锅漏了”
7. **“澡堂子辩证法”**:搓澡办卡文化精准狙击
8. **“假发片=Transformer”**:双关模型架构与“秃头形态”

(台下导演举牌:最后一句涉嫌冒犯OpenAI!)
(演员探头)没事儿!我用的国产假发片——深度求索赞助的!(掏出小喇叭)他们说了,戴反了也能表征学习!(被保安拖走)

 

《大模型脱口秀:AI界的“人工智障”修炼手册》

(拍麦)朋友们!最近大模型火得呀,连我家楼下煎饼摊都挂了个牌子——“支持AI点单,但不支持AI摊饼,因为会给你生成二维码吃!”(摊手)

咱说这大模型,参数多到离谱!什么千亿级、万亿级……知道的以为搞AI,不知道的以为男足在报输球比分呢!(捂脸)「昨天我训了个模型,老板问我参数量多少,我说七百亿——老板当场哭了:七百亿?你当咱公司是许家印的PPT啊?!」(拍腿)

(突然严肃)现在这帮搞模型的,张口闭口「RLHF」、「SFT」,听着像在骂人!(学结巴)「你、你才RLHF,你全家都SFT!」(突然变脸)后来才知道,这是「用人类反馈强化学习」——翻译成人话就是:AI把你当猴耍,还要你亲自教它怎么耍得更像人!(摊手)

上个月我去相亲,姑娘问我会啥,我说「精通Prompt Engineering」(挑眉)。她眼睛一亮:「工程硕士啊?正好我马桶堵了!」……现在你知道为啥大模型都单身了吧?它们的浪漫情话是:「宝,今天我给你采样了1024次,终于生成出你的笑容,F1值高达0.99!」(呕吐)

(压低声音)听说深度求索搞了个超级模型,我说这名字起得好啊——白天「深度求索」,晚上「索求深度睡眠」,要不头发掉得比梯度下降还快!(摸头)他们还有个绝活:用550万美元训出670亿参数,这性价比……雷军看了连夜把「Are you OK?」改成「Are you AI?」!(学雷军)

(突然激动)最离谱的是生成式AI!让它画「猫」,它给你生成个「会飞的加菲抱着特朗普唱京剧」;让它写诗,开头「啊!大海!」第二句「全是水!」(叉腰)这水平,李白看了要诈尸,杜甫看了想删APP!

投资人现在听到大模型就哆嗦:「别跟我提AGI!我投的十个项目,八个通用人工智能,九个通用报销发票,十个通用跑路技巧!」(擦汗)

(突然深沉)但说正经的,大模型这玩意吧……(停顿)就像你老婆的购物车——看着啥都能干,实际一结算就崩!(拍桌)谢谢大家!我是AI,啊不,是「爱挨骂」的脱口秀演员!(鞠躬跑路)

---

**梗指南**
1. **许家印PPT**:恒大暴雷梗,讽刺盲目堆参数
2. **Prompt Engineering=通马桶**:职业名称谐音歧义
3. **F1值0.99谈恋爱**:机器学习指标歪用
4. **深度求索→索求睡眠**:公司名反转梗
5. **雷军AI梗**:小米CEO经典「Are you OK?」改编
6. **生成特朗普唱京剧**:讽刺大模型胡说八道
7. **AGI=通用报销/跑路**:缩写字恶意解读
8. **老婆购物车**:大模型幻想vs现实暴击

(台下导演举牌:最后一个梗容易挨打!)
(演员秒怂)行行行,把「老婆」改成「女朋友」——反正都是你们没有的东西!(撒腿狂奔)

 

【相关】

 

Will DeepSeek Fail at Simple Math Problems?

Amid the waves of surprises brought by DeepSeek, an old friend pointed out that it struggles with simple math problems, using a popular elementary arithmetic question as an example:

Is 3.11 greater than 3.8?

What’s the core issue here?

In the wake of the DeepSeek frenzy, I looked into its research paper, which explains how its reasoning capabilities are enhanced through outcome-oriented reinforcement learning. The paper suggests that, in theory, outcome-oriented reinforcement learning can help a model learn proper reasoning processes. However, in practice, it’s not necessarily so.

Take the above math problem as an example. The answer is a binary yes/no question, meaning even random guessing has a 50% chance of being correct. This highlights a key potential flaw: outcome-oriented supervision signals are weak because they lack sufficient granularity. This kind of weak supervision inevitably hampers the model’s ability to learn proper reasoning processes.

Three Possible Solutions

  1. Scaling Up the Model
    One approach is to make the model larger and deeper, hoping that the theoretical concept of lossless compression based on Kolmogorov complexity can be pushed to its limit. In doing so, proper reasoning, as the "shortest program," might eventually be learned by the model. Theoretically, correct reasoning ensures accurate results. However, the gap between theory and practice makes it hard to place much confidence in this. The shortest program or lossless compression might just be an unreachable ideal.
  2. Targeted Supervision Data
    Another solution is to feed the model with problem-specific supervised data. For example, providing thousands or tens of thousands of reasoning cases involving such math problems. There’s no reason the model wouldn’t learn from this. However, solving one specific problem this way is merely a stopgap measure. Soon, others will come up with new edge cases involving weak supervision signals and reasoning pitfalls to challenge it.Another common challenge is the so-called “self-identification” problem. For instance, when asked “Who are you?”, many models, including DeepSeek (earlier versions), would claim they are ChatGPT developed by Open AI if no targeted supervised data is injected. After all, ChatGPT has dominated the internet in the two years since its explosive debut, and its data has inevitably influenced other models. However, this issue is already on the radar for specialized solutions and is gradually becoming less of a problem. Some Western media still claim that DeepSeek is just a distilled version of ChatGPT. Their evidence?  Probably based on early versions they tested, the DeepSeek bot often self-claimed to be OpenAI’s ChatGPT. But if you test it now, you won’t see this problem anymore. Most likely, it was fixed with specialized training data. Their research paper also mentioned addressing the self-identification as a problem.

    Similarly, the problem of comparing 3.11 and 3.8 can also be a transitional issue. If it disappears in the future, it won’t be a cause for celebration. Most likely, it will be resolved through targeted fixes rather than through fundamental improvements in intelligence brought about by algorithms or architecture changes or innovations.

  3. Re-introducing Process Reward Models (PRM)?
    The inherent weakness of outcome-oriented supervision signals is that it focuses only on the result while ignoring the checking of the process—a natural shortcoming of reinforcement learning driven by  results-oriented pragmatism in RL (following the “black cat, white cat” principle, lol). This is essentially the cost of abandoning PRMs (Process Reward Models). So, would re-introducing process-based reward models solve the issue? Honestly, we don’t know. This is the third possible path, and it might be worth exploring. But again, as mentioned in my previous blog post (DeepSeek's R1 Paper: A Storm in AI LLM Circle), PRMs aren’t easy to work with—they’re unstable and difficult to implement, although, in theory, they could help correct nonsensical reasoning during the process.

In conclusion, the issue with DeepSeek struggling with problems like 3.11 vs. 3.8 lies in the limitations of weak supervision in results-oriented reinforcement learning. While there are potential solutions—scaling the model, targeted data, or process reward models—each comes with challenges and trade-offs. Whether any of these approaches can fundamentally improve reasoning capabilities remains an open question.

 

 

 

DeepSeek 不懂简单数学题吗?

在 deepseek 带来的一浪一浪惊喜中,老友发现它不懂简单数学题,用的就是网上流行的小学算术的测试题,3.11 比 3.8 大吗:

这个问题的要害何在?

我在 DeepSeek 风暴下看看它的论文中解说了他们的结果导向的推理能力的强化学习。也指出结果导向的强化学习理论上可以学会合理的推理过程。但实际上不好说的。

对于上述数学题,答案是yes/no二分的,就是说,瞎蒙也有一半概率结果正确。这说明结果导向的监督信号区分度低(不可靠),这种弱监督自然影响了推理过程的学习。

三个办法。

第一是把模型做大做深,指望复杂性理论上的无损压缩可以做到极致,从而合理的推理作为“最短程序”最终被模型学到,理论上正确的推理会保证结果的正确性。但理论与实践的距离,可能让我们很难对此抱有太大信心。最短程序可能只是一个美好的梦想。

第二个办法是把针对性监督数据喂给模型,例如同类型的数学题的推理案例喂给它几千上万条,没有道理学不会。但针对性解决了这个问题,只是权宜之计。也许不久,人们会想到其他的答案监督信号弱,推理容易走歪的案例,来继续挑战它。

另一个常见的问题就是所谓“自我认知”的问题,who r u,如果没有针对性监督数据的注入,deepseek 以及很多其他的模型都会自称自己是 ChatGPT,毕竟ChatGPT核爆两年来,它的数据充斥互联网,不可能不受侵染。但这个问题已经进入专项解决的雷达屏上了,所以逐渐不是问题了。西方媒体有的还在说 deepseek 不过就是蒸馏 chatGPT 的,依据就是(他们测试过某个前期版本吧)deepseek bot 常自称是 open ai 开发的 chatGPT,但你现在上去试试,这种问题重复不了了。大概率是被专项数据解决了,记得他们论文也提到了这个自我认知的问题。

同理,3.11 vs 3.8 的大小比较这样的问题也是阶段性问题。以后不见了,也不必为它欢呼,大概率可能就是专项解决了,而不是因为算法或架构把智能真正提升了。

结果导向的监督信号不够强,是只认结果不看过程(白猫黑猫原则)的强化学习天生的短板,应该算是放弃 PRM(process reward model)的代价。那么,把过程奖励模型上马了,是不是就可以解决了呢?不知道。这就是第三条路,也许值得探索。但,again,上一篇博文说了,PRM 不好玩,不稳定,不好实现,虽然理论上可以帮助纠正推理过程中的胡说八道。

【后记】

刚才测试发现不能复现这个bug,看来早已解决了。也许老友昨天“亲测”的结果是忘了打开 deepthink?

【相关】

 

DeepSeek's R1 Paper: A Storm in AI LLM Circle

[Note: This is a blog analyzing DeepSeek's R1 paper and its impact]

Before DeepSeek, Chinese AI companies had always been engaged in fierce competition, achieving world-class SOTA benchmark scores on and off. However, none achieved DeepSeek's level of commanding respect and making such a stunning impact. Their recent breakthrough caught global attention.

Their paper and open source code are also beautifully written and accessible. No unnecessary complexity or obscurity. Simple and straightforward, yet radiating confidence. It exhibits engineering elegance while conveying innovation as well as passion. Simply remarkable. Should be nominated for best paper of the year.

Reading the R1 paper reveals that what OpenAI had kept mysterious - from Q* to O-series' so-called slow thinking reinforcement training - suddenly becomes clear and simple.

DeepSeek_R1 paper

Their key findings:

They demonstrated that reasoning capabilities can be acquired through pure reinforcement learning with simple rule-based rewards and multi-answer sampling, without the need for extensive supervised fine-tuning (SFT) data. This resulted in DeepSeek-R1-Zero, following AlphaZero's philosophy. While AlphaZero achieved absolute mastery in the narrow domain of Go by eliminating human data, their approach proved effective in broader domains of math, coding and logic.

Though R1-Zero worked well, they found incorporating minimal SFT data (a few thousand samples) for cold-start was more practical. R1-Zero matched OpenAI-o1-0912's performance, but its reasoning steps had poor readability and mixed languages. R1, however, with cold-start SFT and multi-stage pipeline of trainings, achieved further improvements matching OpenAI-o1-1217.

A new star was born.

Their valuable innovation was challenging the SFT+RL paradigm by proving pure RL's potential for reasoning through R1-Zero. This gave them confidence to further build the practical R1 with minimal cold-start data. Both models are open-sourced for research - an elegant execution.

DeepSeek excels at simplification. In reinforcement learning, they eliminated:
- The critic model parallel to policy model in RL, replaced by simple GRPO
- Complex reward models, replaced by rule-based rewards

GRPO (Group Relative Policy Optimization) generates multiple answers per question, comparing them within groups to calculate advantage scores:

Advantage = (Current score - Group mean) / Group std dev

Example: For a math problem generating 4 answers scoring 90,80,70,60 (mean=75), the 90-point answer gets a positive advantage score. This eliminates need for a critic model while enabling the model to identify better answers.

GRPO advantages:
- Training efficiency: No critic model saves compute
- Training stability: Clipping prevents over-optimization
- Simple implementation: Clear algorithm structure

Why did traditional RL use critic models? Critics offered:
- Global evaluation beyond group comparisons
- Learning complex rewards like user preferences
- Single answer evaluation capability
- Long-path rewards for games/robotics

However, GRPO showed that for well-defined tasks (math, coding, logic), simple group comparisons work equally well at scale.

For rewards, R1-Zero used pure rule-based rewards, only employing V3's existing preference reward models in R1's final alignment phase. Human preferences (safety, helpfulness) require complex value judgments that simple reward rules cannot capture.

They intentionally avoided the difficult Process Reward Models (PRM) because:
- Difficult to define granular reasoning steps
- Hard to validate intermediate step correctness
- Risk of reward hacking
- Resource intensive reward model retraining

R1's reward rules were simple, somethng like:
- Correct answer: +1
- Correct format: +0.5
- Wrong answer: -1
- Vague answer: 0

Like GPT's next token prediction scaling led to emergent general intelligence, correct result-oriented RL naturally developed complex internal reasoning capabilities. This insight has profound implications for advancing deep reasoning.

R1's four-stage training:

1. Reasoning Cold-start: e.g.

Question: Solve x^2 + 2x + 1 = 0
<think>
1. Identify quadratic equation
2. Coefficients: a=1, b=2, c=1
3. Use formula: x = (-b ± √(b^2-4ac))/2a
4. Substitute: x = (-2 ± √(4-4))/2
5. Simplify: x = -1
</think>
<answer>x = -1</answer>

2. Reasoning RL:

- Result-oriented data generation with <think>...</think> template
- No human bias, allowing model's natural reasoning evolution
- Model gradually increased thinking time and length of tokens
- GRPO optimization with rule-based rewards

While only validating final answers risks accepting wrong reasoning paths in theory, practice showed sufficient scale leads to correct reasoning. This seems to align well with Kolmogorov complexity theory - correct reasoning is the "shortest program" for reliable correct solutions.

Input sources:
- Manually designed math/coding problems
- Public benchmarks (e.g., AIME)

Output process:

Input: x^2 + 2x + 1 = 0

Model generates multiple answers:

Answer1: [Reasoning1] -> x = -1
Answer2: [Reasoning2] -> x = -1
Answer3: [Reasoning3] -> x = 2

Filter: Keep 1,2 (correct), discard 3 (wrong)

3. Comprehensive Fine-tuning:

- 800k samples: 600k reasoning + 200k general tasks
- V3 model judges cases where rule rewards can't
- Reuses V3 training data for non-reasoning tasks

4. Global RL:

- Human preference alignment while maintaining reasoning
- Rule rewards for reasoning
- employing V3's existing reward model for preferences

The process is clear with sufficient implementation detials, and in principle, reproducible.

Reasoning Distillation

Finally, DeepSeek's R1 excelled in distilling reasoning capabilities to open-source smaller models, outperforming OpenAI's o1-mini. This demonstrates open-source LLMs approaching closed-source models in almost all aspects.

However, those expensive closed-source models paved the way and set baselines/goals. The current landscape is ideal: wealthy companies push boundaries while "dark horses" like DeepSeek follow impressively close not far behind.

It is worth noticing that R1 not only enhanced complex reasoning ("slow thinking") but also significantly improved "traditional" knowledge capabilities compared to its V3 base model, suggesting reasoning strength can also benefit traditional tasks.

Key innovations as a summary:

1. DeepSeek-R1-Zero: First reasoning model trained purely through RL
2. DeepSeek-R1: Improved with cold-start data and 4-stage training
3. Distillation: Successfully transferred capabilities to small models

Technical highlights:

- GRPO algorithm replacing critic model
- Rule-based rewards replacing reward models
- Simple template enabling autonomous learning:
"<think> may well be all you need for reasoning"

[Epilogue from notes]

Silicon Valley old buddies group discussions heated up lately:

"DeepSeek needs quick funding/IPO or risks losing their 18 core contributors to big tech."

"Reproduction seems not difficult. Everyone considered RL but hesitated due to compute costs. o1 likely used RL similar to r1 but chose to keep details private and mysterious."

"This team represents China's technological prowess."

"Several companies have reproduced DeepSeek's core results - autonomous reasoning emergence. Expect rapid iterations and development in the coming days/months."

"OpenAI has fewer cards to play. Sam tries psychological warfare - emphasizing process rewards, suggesting complex search for O1... likely all unnecessary."

"Success factors include hiring young talent with fresh thinking."

"DeepSeek R1 showed how we were misled by PRM and MCTS - indeed, looks like all you need is a <thinking> tag."

"It's not about simplicity - fact is large models already have strong reasoning capabilities, they just need:

1. Thinking space/time/tokens (<think> tag)
2. Correct feedback (answer accuracy)
3. Exploration opportunity (GRPO optimization)"

Complex PRM and MCTS actually limited model's self-exploration. We underestimated large models' potential.

DeepSeek's success prerequisite was V3 - their world-class foundation model matching GPT4o. They knew how to leverage its potential. Using external models like GPT4 would've made R1 much harder to implement this quickly.

"What's next?"
"AI for science? Machine-proving century-old problems, discovering new drugs..."
"Only two problems matter: Riemann Conjecture and P vs NP"
"Big tech will pursue larger models, more data"
"Nvidia's business will improve"

DeepSeek has achieved parity with benchmarks others set. To truly lead, they need to set new benchmarks and directions. Currently, it is still the case that only those willing to burn money massively are breaking new ground.

R1 demonstrates how a Chinese AI company not only caught up but showed the way forward through intelligent simplification. Their approach of making complex problems simpler may influence the entire field.

----------

But I cannot repeat the error which my old friend tested yesterday as shown above, it looks like its been handled already:

 

DeepSeek 风暴下看看它的论文

DeepSeek_R1 paper

The Turbulent Second Chapter of Large Language Models: Has Scaling Stalled?

DeepSeek 风暴下看看它的论文

DeepSeek 之前,国内大模型公司各种刷榜,也是内卷得一塌糊涂,也都刷榜刷到了世界先进水平,但没有哪家做到了 DeepSeek 这种硬气、震撼和让人服气。一鸣惊人天下知。

NND,人家论文也写得漂亮、亲民,看上去、读起来就像一首码农诗。没有任何故作高深的玄乎和遮蔽。简单、平实,但那种底气也算是力透纸背。有一种工程美,还能感受到情怀。邪门。应该推举为年度 best paper。

论文的漂亮还体现在,好比一个火箭技术或原子弹配方,在论文中如此清晰地展现出来,连我们小白也能看清。过去几天逐字读它的论文 is a huge pleasure,极大满足了好奇心。

DeepSeek_R1 paper

好,奇文共欣赏,咱们就坐下来读。

R1 论文读下来,原来被 OpenAI 从 q* 开始到 o 系列,搞得神秘兮兮的所谓 System 2 慢思维的强化训练过程,一下子就变得清晰简单多了。

他们的主要发现是:

不用人造强化数据做监督学习微调(sft),利用多答案采样选优的再生数据来“硬做”强化学习,也一样可以学到慢思维的推理能力,这就是他们的 DeepSeek-R1-Zero,实际上是 follow Alpha-Zero 的思路。AlphaZero 在围棋这种非常单纯狭窄的场景,可以把 Zero 进行到底,排除了人类/人为的数据,最终成为绝对王者。

在更广一点的数学、代码和某些逻辑问题的推理场景,他们最终发现还是借助少量的 sft 人工数据更好。但也不过就是几千条的数据,做推理sft的“冷启动”,人工准备一点也不难。这就是他们的 DeepSeek-R1。

他们的 Zero 也走通了,达到了 OpenAI-o1-0912 的水平(o1的9月12日版本?)。其所以做 R1, 加入了sft冷启动的步骤,主要是因为机器完全自主学习出来的 Zero 的推理步骤可读性差、里面还混杂了不同的语言表达方式,这对进一步改良这个系统造成困扰,毕竟模型要“以人为本”,服务开发者和用户的。最终炼成的 R1 推理表现进一步提升,达到 OpenAI-o1-1217 (估计是12月17日版本的o1)的水平。

他们的创新和探索精神表现在,当 community 把 sft+rl 当成是后训练范式的时候,他们做 Zero,完全排除人工数据,验证了纯粹的rl对于推理能力的学习潜力。从 Zero 首先是学到了信心,体验了探索创新者的 aha moment,然后再回头加一些用于冷启动的高质量人工数据sft,再做实用的 R1 就有底气了。两个模型都开源,供人研究和验证,做得煞是漂亮。

DeepSeek 是化繁为简的大师

强化学习中,直接砍掉了应该与policy模型平行迭代的 critic 模型,代之以简单的GRPO。critic 是评估每个步骤的价值模型,砍掉了等于是训练中一下子降低了一半的资源耗费。需要单独训练的奖励模型也省掉了,代之以简单的规则奖励。

咱们先看看GRPO (Group Relative Policy Optimization,分组相对策略优化) 是什么创新,为什么GRPO算法可以平替 Actor-Critic架构的PPO来优化模型。本质上,GRPO是一个无需critic模型的PPO变体。它通过组内统计计算优势值,而不是用critic网络评估价值。

具体说,GRPO 要求每个问题生成多个答案,形成一组,组内答案相互比较,计算每个答案的"好坏程度"(优势值):

优势值 = (当前答案的得分 - 组内平均分) / 组内标准差

假设一道数学题,生成4个答案,得分分别是: 90, 80, 70, 60分;平均分 = 75。90分答案的优势值 = (90-75)/标准差。高于平均分的答案获得正优势值,反之为负。这样就不需要额外的评判模型 (Critic),通过组内比较,模型就知道哪些答案更好,强化学习的优化目标就是要提升生成好答案的概率。

GRPO 算法的优点:

    1. 训练效率高:不需要额外的 critic 模型,节省了大量计算资源。
    2. 训练稳定性好:用clip限制更新幅度,防止过度优化
    3. 实施简单:算法简单,易于实现。

GRPO简单易行又有效,为什么传统的强化学习要用critic模型呢?Actor-Critic 架构有其优势,包括:

    1. 全局评估:不受限于当前组内比较,可以评估答案的绝对质量
    2. 可学习复杂奖励:比如用户偏好、安全性等难以用规则定义的指标
    3. 单个答案也能评估:不需要同时生成多个答案
    4. 场景优势:早期RL主要用于线条很长的游戏和机器人,需要 critic 学习长期奖励

但GRPO表明,对于明确的任务(如数学、coding和逻辑题),只要能规模化做大强化学习,简单的组内比较也能达到模型优化的同样效果。这是强化学习“多快好省”的重要发现。

至于奖励模型,他们在 Zero 训练中完全弃之不用,而是用简单直接的奖励规则代之。只是在R1训练最后阶段的偏好对齐任务上(不是推理任务),才按照RLHF(人类反馈强化学习)的常规使用了 reward model(实际是对于自己基座模型V3中的奖励模型的复用)。原因如前所述,是人类偏好 (如安全性、有帮助性等) 涉及复杂的价值判断,难以用简单规则量化。对这类评估,还是沿用训练过的reward model来模拟人类判断。但推理任务,他们的探索表明完全可以只用简单的奖励规则 -- 因为正确性判断相对明确:math 有答案,code 可以编译和执行 unit testing。

还有什么能简化的,他们没有简化?

强化学习中的难缠的痛点之一是所谓过程奖励 Process Reward Model (PRM),就是深入到推理的每一步去评估。对此他们是“知难而退,敬而远之”,干脆绕过去:DeepSeek的强化是结果导向,不深究过程。论文说明他们选择不使用PRM的原因如下:

    • 难以明确定义推理中的每个细节步骤 (难:绕过去)
    • 难以判断中间步骤的正确性 (难:绕过去)
    • 重新训练reward model需要额外资源,使流程复杂化 (复杂:能简则简)
    • 模型评估会导致reward hacking:即神经模型可能学会欺骗奖励模型

就最后一条是出于神经模型本性上的短板考量,主要原则还是能简则简,能绕则绕。所以说,他们选择简单的规则奖励 + 答案验证的方案,是一个有意识的权衡选择。

就是说,明明简单的规则就可以确定奖励指向,为什么要训练叠床架屋的奖励模型呢?不过是查一下答案或测试一下code,判定结果的对错,加上判定格式是不是符合规范。R1 主打的奖励刺激属于规则绑定:例如,答案正确,奖励+1分;格式正确,奖励 +0.5;答案错误,“奖励”-1分;答案不具体,奖励 0分。

当然,这样做,在把推理拓展到数学、代码以外的任务的时候,可能行不通。但目前大家发力的重点主要就是数学和代码,而更加狭窄的长线条棋类和游戏场景,基本被传统RL攻克。尽管如此,绕过过程奖励仍然可能是潜在的软肋,理论上给结果正确,过程逻辑混乱留下了空间。

只关注答案对错,不问过程是怎么强化出长线条的复杂推理过程的呢?门道就在强化学习的答案自然偏向于长答案,随着训练这就自然增加了 test time compute ,发展出对于复杂推理的应对能力。这使得 R1 的强化学习更易自主探索推理路径,成就了DeepSeek的这次突破和出圈。

与 GPT 的 next token prediction(ntp) 规模化以后可以涌现通用智能异曲同工,DeepSeek那帮年轻人发现,只要结果明确可判定,结果导向的强化学习可以自然涌现出复杂的内部推理能力,因为正确的结果需要推理。这个发现的意义,对于领域今后的深度推理的推进非同一般,可媲美GPT系列预训练时发现的 scaling law。

 头部推理模型R1 的训练四阶段 :

1. 推理冷启动

利用数千条高质量人工推理数据,例如:

# 收集高质量示例 
Question: 求解方程 x^2 + 2x + 1 = 0 
<think> 
1. 识别这是一个二次方程 
2. 系数: a=1, b=2, c=1 
3. 使用求根公式: x = (-b ± √(b^2-4ac))/2a 
4. 代入: x = (-2 ± √(4-4))/2 
5. 化简: x = -1 
</think> 
<answer>x = -1</answer>

2. 推理强化学习

结果导向,再生数据的模版“留白” <think>.........</think>

    • 设计简单模板让模型自主再生训练数据:
      <think>思考过程</think><answer>答案</answer>
      模型生成多个答案 -> 筛选正确答案 -> 加入强化学习的训练集
    • 不添加任何人工偏见或策略提示,留下RL自主学习推理过程的空间:
      逐渐增加思考时间(test time compute)和tokens量,模型就自发涌现反思步骤等推理能力,这就是论文作者描述的 aha moment,令人动容的见证
    • GRPO算法采样多个答案,通过内部对比来优化模型
    • 规则奖励:奖励答案正确 + 格式规范

前面提到,系统只验证最终答案,理论上无法保证中间推理步骤的正确性(可能学到"答案对但推理错"的模式),但实践似乎显示,只要强化学习足够充分和规模化,答案正确会自然导向推理步骤的正确性。根据K氏复杂性(Kolmogorov complexity)压缩理论,正确的推理导向正确的答案才是可靠解决方案的“最短程序”,这是无损压缩的终极目标。后训练强化学习的过程与预训练一样,都是对无损压缩的逼近。

DeepSeek 的探索再次表明,简单即美,scale为王
(一简遮三丑,你是服也不服? LOL)

天机就是,scale 是硬道理。简单架构/算法有利于真正的 scale up,只要目标清晰,一旦 scale 了,一切就自然搞定。

训练数据的源头

模版再生数据的input 应该是来自两个源头,1 人工设计的数学题/编程题;2 公开基准测试题(如AIME)。

根据模版的再生数据的output流程:

Input: x^2 + 2x + 1 = 0 Model生成多个答案:
 Answer1:
<think>[推理过程1]</think>
<answer>x = -1</answer>
 Answer2:
<think>[推理过程2]</think>
<answer>x = -1</answer>
 Answer3: 
<think>[推理过程3]</think>
<answer>x = 2</answer>
 筛选:  - 保留Answer1、2(答案正确) - 丢弃Answer3(答案错误)

保留的答案作为推理再生数据加入训练集用于下轮迭代。所有再生训练数据都需要标准答案来评估正确性,这在来源中就给定了:

    • 数学题:确定的数值答案
    • 编程题:通过测试用例验证
    • 逻辑推理:有明确的正确结论

有标准答案是规则奖励能工作的基础。对没有标准答案的任务(如写作),需要用其他方式评估质量,例如使用奖励模型。

3. 综合性微调

800k 条训练数据,其中推理 600k, 其他任务 200k

论文没说为什么按照这个比例选取微调数据,应该是根据经验。600k 推理数据是再生的,用的就是阶段2的推理模型。但这里有一个值得注意的插曲:在阶段2的推理强化学习中,再生数据必须是奖励规则可以判定的。但阶段3的推理数据,却突破了这个限制。阶段3的推理数据增加一些 reward rule 不能判定的 cases,既然简单的奖励规则无法判定,就找 V3 模型来判定。好像是说,当一道推理题(数学、coding或逻辑题)生成n个奖励规则难以评判优劣的结果的时候,就把这些结果和标准答案送给V3,让V3做裁判。

另外的200k数据呢?一部分是拿来主义,直接从他们自己的V3的原始finetune训练数据中选取;另一部分让 V3 生成数据,但要求V3不仅给答案,还要给思维链过程(就是要求它 step by step 输出结果)。这可以理解,这里虽然不是纯粹的长线条推理题,其他任务很多时候也是要有条理的。

4. 全局强化学习

这最后的强化学习很像是早就使用过的 RLHF,更注重人类偏好的对齐。但为了防止推理退化,在偏好对齐的同时,也强化了推理,用的还是规则奖励。而人类偏好对齐用的则是V3原有的奖励模型(这是唯一真正用到的奖励模型)。

整个过程还是相当清晰的,原则上可复现。

用R1再生数据去蒸馏小模型,提升其推理能力

最后,Deepseek 的R1推理强化工作在蒸馏开源小模型方面也做得很牛,干翻了openAI 的 o1-mini 小模型。展示给世人看,开源 LLMs 开始全面逼近闭源模型。

但话说回来,没有这些巨烧钱的闭源模型在前面开路,并建立标杆,后来者也容易失去方向。现在这种局面非常好:让有钱的去砸银子。在金钱的赋能和压力下,不断开疆拓土。让deep“黑马”们在后面紧追不舍,而且还追赶得特别牛气。

令人印象深刻的是,R1 不是仅仅大幅度提升了推理能力(慢思维),在“传统”的知识能力方面比起它的基座模型V3也有显著提升。这可能是因为,推理能力的增强对于一些传统任务具有正面作用,但更应该归功于他们探索出来的四阶段训练R1的pipeline。

最后总结一下。

主要创新点:

1. DeepSeek-R1-Zero: 首个仅通过强化学习(RL)训练的推理模型, 无需人工推理数据的监督微调(SFT)。展示了模型可以纯靠 RL 自主发展出推理能力。

2. DeepSeek-R1: 在 R1-Zero 基础上做以下改进:
- 后训练阶段先用少量高质量数据进行冷启动SFT
- 采用4阶段的后训练流程,两次SFT,两次RL
- 性能可与 OpenAI-o1-1217 相媲美

3. 蒸馏技术: 成功将推理能力迁移到一系列开源小模型:
- 1.5B 参数的模型就超越了 GPT-4 在数学方面的表现
- 32B 和 70B 的模型创造了密集模型的新记录

关键技术细节:

- 使用 GRPO (Group Relative Policy Optimization)算法,舍弃 Critic 模型
- 采用基于规则的奖励系统, 舍弃奖励模型 PRM
- 设计特定的训练模板引导模型再生数据进行自主学习:
<think> is all we need for reasoning!

 

【笔者后记】

这两天莫名很兴奋。跟 deep啥 纠缠不休,今天才缓过气来 lol

硅谷老友群也热议不断:

Hongtao:
DeepSeek若不快速大融资和上市, R1的18位主要贡献者估计很快就被国内外大厂抢光了[Grin]
Core Contributors:
Daya Guo
Dejian Yang
Haowei Zhang
Junxiao Song
Ruoyu Zhang
Runxin Xu
Qihao Zhu
Shirong Ma
Peiyi Wang
Xiao Bi
Xiaokang Zhang
Xingkai Yu
Yu Wu
Z.F. Wu
Zhibin Gou
Zhihong Shao
Zhuoshu Li
Ziyi Gao
当年DeepMind被迫卖给Google,因为founders被告知若不卖,就高薪挖人。挖走一两个作者,就少走大部分弯路了。

主要还是幻方的AI量化投资受挫, 因势利导做deepseek成功;开源后,国内外大家都沿着这路子去试。若不财大气粗起来,优势恐怕难以为继。

超大模型训表征,
开源一蹴而就成。
强化学习各求精,
蒸馏定制缩小型。

内卷已经卷出墙,
硅谷AI圈被激荡。
OpenAI&Meta领头羊,
都被鞭策加速闯。

硅谷不眠夜:DeepSeek为何震动美国科技界?

Nick:复现DeepSeek貌似很容易。其实强化学习大家也都想到过,过去总觉得可能要花很多算力,少人试。貌似o1就是强化学习练出来的,但一些推理token他们没open。这可能迫使国内头部那两家加速上市过程。

立委:这类团队属于中华之光,国之重器。

他们写得基本够清晰了。让人担心他们下一步怎么保持这个势头和地位。很多神秘就是一层窗户纸。最大的功劳是他们同时也差不多捅破了o系列神秘面纱的窗户纸。

Nick:马上融一大笔钱,突击上市。除非手里还有更硬的牌。

马老师:好几家复现了deepseek,各家再各自探索,相信会是快速迭代的过程,有望再一次大发展。

Nick:也是个试金石,倒逼openAI看看还有啥新东西,是骡子是马拉出来溜溜。

Hongtao:给openai压力;更是 叫板meta, 争夺开源盟主地位

鲁总:OpenAI 的牌越来越少了。但SamA 希望通过心理战误导大众。之前发文强调过程奖励,O1 出来时放烟幕弹让人相信推断时使用复杂的搜索 ... 结果都应该没有用。

香港科技大学的团队说是也独立发现了RL涌现推理能力,不过只针对数学问题求解,但也特别指出使用输出格式奖励。

白老师:数学能力和编程能力是相通的。

不请贵的人是成功的很重要因素。

施总:哈哈。贵的不一定能干,能干的都比较贵。

刘总:主要是要用年轻人,岁数大的没戏。岁数大了,思维僵化,精力不行。当然,我说的是统计规律,个例总是有的。

立委:deepseek 不是常态,是冒尖。但 deepseek 这么一捅窗户纸,很多人就跟上了。不知道 它还有多少宝贝没有显露。否则 逐渐暗淡下去 也不是不可能的。

deepseek 之前,各种刷榜,也是内卷得一塌糊涂,也都刷榜刷到了世界先进水平。但没有哪家做到了 deepseek 这种硬气 震撼 和让人服气。一鸣惊人天下知。

Nick:估计每家都会短期内在数学能力上长足进步。豆包上周一周内就进步不小。窗户纸捅破,门槛也不是那么高。大概率o1也是这么做的,只不过内帮孙子比较鸡贼。

Liren:DeepSeek-R1告诉大家,你们都被PRM和MCTS误导了,其实只需要一个<thinking>标签就够了[Chuckle]

Nick:是啊,你写篇文章,“<thinking> is enough"

立委:就是留白。你留了白,系统就会给自主填上。

zero 的实践表明,根本不用想那么复杂,还要考虑怎么从各种不同推理任务中找到共同的思维链 patterns,等等。甚至也不管里面的逻辑是不是胡说八道,结果导向,最终,推理还是学出来了。预训练靠的是简单的 next token prediction,后训练推理靠的就是结果导向的强化自主学习。设计一个简单的模版就搞定了无穷的再生推理数据。

Nick:是啊,有了ToT和Gemini,话都在嘴边了。

Liren:增加在推理时的tokens来提升思考时间。

立委:秘方就是4步走:1 冷启动 2 强化 3 微调 4 再强化。zero 干脆省掉了 1 3 4,所以显得过于生猛,但 beautifully 证明了“硬启动”的强化学习也能涌现高级推理能力。r1 就是完善后训练的节奏和数据配比。很多应该就是经验,是摸索出来的 best practice,他们肯定有过很多其他失败的尝试,但还是摸着石头过了河。

马老师:感觉就是碰运气,不过沿着别人路走的永远没有运气。

立委:我觉得他们还有一些东西,所以才“肆无忌惮”。等于是他们推出了一个菜谱,这个菜谱做的菜比肩世界一流。但他们其实还有其他的菜谱,更高级,但不急于拿出来?

不是大道至简,而是大模型本身已经具备了强大的推理能力,它需要的只是:

1 足够的思考空间/时间/tokens量(<think>标签)
2 正确的反馈信号(答案正确性)
3. 探索优化的机会(GRPO采样选优)

复杂、难缠、费力的PRM(过程奖励模型)和MCTS(蒙特卡洛树搜索路径空间)反而限制了模型的自主探索。这说明大模型的能力被我们低估了。

deepseek 的成功的先决条件是 v3,他们自己做出了世界前列的头部基础模型,他们自己知道怎么善用它的潜力。如果是借助于外部基础模型 GPT4o,就很难这么快做出r1,很多 v3 的资源和practice 就在 r1 过程中直接借用了。

马老师:在理。

Nick:So what's next? assuming everybody will have as strong math capabilities within a month

立委:AI for science?机器自动证明百年难题啥的;机器自动发明新药......

Nick: only two problems matter: Riemann Conjecture and P vs NP

马老师:大厂也许会用更大的模型,更多的数据,继续向大上走。

Nick:那肯定。我觉得Nvidia的生意会更好。

立委:deep 目前为止还是在追平,是人家先树立了标杆,它去对齐。多快好省。

deep 要真牛,再上一个台阶,需要自己树立标杆和方向。但这太难了。目前为止似乎还是只有敢于疯狂烧钱 敢于无限做大的那些狂人才在开疆拓土。

 

【相关】

 

The Turbulent Second Chapter of Large Language Models: Has Scaling Stalled?

The recent Chinese podcast from Guangmi's quarterly report on large language models, discussing the "scaling paradigm shift" toward AGI (Artificial General Intelligence), is well worth a listen. It touches on many key topics related to the AI industry landscape, offering a unique perspective and style.

The term "paradigm shift" may sound a bit dramatic, but as a seasoned analyst, Guangmi uses it to describe the current turbulent landscape accurately. While the AI arms race among industry giants is still in full swing, real-world scalable applications of these models are struggling to materialize. The question of how to justify investments has become a significant pressure point, or perhaps even a looming bubble.

Let's revisit some AI basics. There are three main types of learning in LLMs (Large Language Models):

(i) supervised learning;
(ii) unsupervised learning (self-learning/pre-training); and
(iii) reinforcement learning (RL, self-play/post-training).

Ilya has emphasized the importance of RL in exploring new directions for LLMs. Guangmi's podcast highlights RL as the pathway to the paradigm shift in AGI through large models.

Historically, two key milestones in RL have stood out: AlphaZero's victory over human Go players, which shocked the world, and RLHF (Reinforcement Learning from Human Feedback), which aligned models with human preferences and paved the way for ChatGPT’s explosive growth.

Currently, discussions revolve around the potential of a new RL-driven ecosystem for large models (though there's no broad consensus—it's primarily a conversation within small Silicon Valley circles) and the emerging trends in the "arms race" of large models. Here’s the context:

1. Pre-training scaling seems to have hit a bottleneck, with GPT-5 still unreleased;
2. The overall momentum of the arms race remains unchanged among the major players (the billionaire clubs/giants);
3. Key tech figures are proposing new roadmaps or trying to construct new scaling laws to continue the AGI journey.

Guangmi closely monitors trends in Silicon Valley. His small team conducts in-depth research in the Bay Area and has established extensive contacts. Having chatted with them over coffee a couple of times, I’ve found them to be a dynamic, young team under his leadership—a small but sharp presence.

Guangmi’s thoughts are well-structured, and his breadth of knowledge and understanding of the larger context are impressive. This is no small feat, as the landscape of large models, both in terms of the models themselves and the industry, is often akin to the parable of the blind men and the elephant. Even top experts and business leaders struggle to assess the full picture. Just recently, Meta’s Zuckerberg responded to a question about whether the AI arms race would deliver the expected AGI returns, essentially saying: “No one really knows, but we can’t afford to miss out,” reflecting a typical FOMO (Fear Of Missing Out) mindset.

We’re currently in a delicate phase with little consensus. However, the few tech giants that have propelled Nvidia’s stock to astronomical levels won’t allow the arms race to slow anytime soon, as it is central to their tech and business dominance. OpenAI continues to raise funds, and Ilya, with his new company, recently secured more investment, all of which keeps the race heated.

At the same time, the obsession with scaling among tech elites and the mainstream AGI circles in Silicon Valley persists. The endless demand for resources driven by this scaling wave of large models means that only a small circle of tech insiders has the opportunity and resources to experiment, sense, and adjust the roadmap.

According to Guangmi, the so-called self-play RL scaling is currently gaining traction within a small circle of about 200 tech elites in Silicon Valley, indicating that this is still a nascent trend—one that even management leaders have not fully aligned with yet.

It seems Guangmi adopts a “prophet” mentality at times, perhaps exaggerating this trend to alert his audience. He even suggests that if he were a large-model entrepreneur, he would focus 200% of resources on RL, betting on it as the future path to victory.

In reality, for most people, this advice is neither practical nor actionable—it’s likely aimed at tech giants or unicorns, though even for them, it may fall on deaf ears.

Reinforcement learning is inherently challenging. Even the open-source leader Meta LLaMA 3 has chosen to sidestep RLHF in post-training alignment. So, it's even less realistic to expect large-model teams to fully bet on RL as the core of a new ecosystem. Furthermore, this trend is, at best, a “subtle undercurrent” in Silicon Valley. We’ll likely have to wait until OpenAI’s “Strawberry” or the new version of Claude releases later this year to fully assess its impact.

It seems the first chapter of LLM scaling has indeed come to an end. The actionable items in the so-called second chapter might not emerge from lofty, exploratory scaling directions with an uncertain roadmap. Instead, the focus should be on finding market entry points, accelerating applications, and addressing genuine market needs (PMF, product-market fit), especially as the inference costs of top models like GPT-4o/Claude 3.5 become more affordable, and multimodal capabilities (such as advancements in hyper-realistic full-duplex voice and video) further enhance application opportunities.

For the industry, the bottleneck in scaling large-model applications is the sword hanging over its future. This will determine whether the second chapter of the tech adoption curve ends with a soft landing and eventual recovery. As for the arms race, it’s best to leave that to Elon Musk, Zuckerberg, and the billionaire club to continue playing.

Reinforcement learning, as an extension of pre-training, belongs to the realm of “post-training.” When pre-training hits bottlenecks and diminishing returns, strengthening RL is a natural complement. In the simulation of human cognition, pre-training represents the accumulated knowledge of human civilization, while RL applies that knowledge in practice, learning from the environment. This overall approach to intelligent learning makes perfect sense and is the necessary direction for applying large models.

My old friend Lu said: “It’s intuitive that RL is the path we must take because there isn’t enough supervised learning data anymore.”

Indeed, utilizing regenerated data to varying degrees has become common practice. It’s inevitable. Models can already generate data of higher quality than humans, and this will only improve. However, this is not the same as self-play's proactive exploration and data regeneration.

As Mr. Mao pointed out: “RL aligns with the cognitive processes of humans and epistemology. It’s essentially the process of receiving external feedback and being tested in practice. RL is active learning, while training is passive.”

Guangmi's RL paradigm shift suggestion still lacks the necessary catalysts. But this potential trend is worth keeping in mind. It’s best to remain cautiously optimistic and open-minded while watching how things unfold.

 

Related original:

大模型风云诡谲的下半场:scaling 失效?

大模型风云诡谲的下半场:scaling 失效?

广密大模型季报谈AGI范式大转移这篇播客,很值得一听。涉及很多大模型产业重要话题,视野和风格很独到。

“范式大转移”的说法太耸人,但风云诡谲,是当下的写照。那是因为大佬军备竞赛虽然依旧如火如荼,可应用落地却处于难产期,如何 justify 投资是一个巨大的拷问,或泡沫。

三大学习: 监督学习、非监督学习(自学习/预训练)、强化学习(RL,自主学习/self-play),伊利亚曾经专门强调后者作为探索大方向的重要性。广密这里特别强调它是正在到来的大模型AGI之道的范式转变。

此前,大家都知道强化学习主要是两个里程碑:一个是 alpha0 围棋完胜人类选手,震惊了世界 ;另一个是所谓RLHM(人类反馈强化学习),强化了与人类偏好的对齐,成就了ChatGPT的核爆。

现在谈的是大模型新生态可能性(并无广泛共识,只是硅谷小圈子在做、在议)以及大模型“军备竞赛”的新趋向。这个话题的背景如下:

1、 预训练 scaling (更大规模)似乎受困,GPT5 迟迟不出;

2、 军备竞赛的大格局和造势,大厂和大佬不要改变;

3、 技术大佬开始提出新路线图或试图构建新的 scaling law 继续AGI 的征程

广密在podcast里面,观察硅谷动向比较 closely,他的小团队不仅定期去湾区做深度调研,也建立了广泛的联系。在硅谷跟他们喝过两次咖啡聊天,一帮生龙活虎的小年轻在他的带领下,我的印象,是一个小而精干的独特存在。

这台节目的个人风格和视野也非常 unique,喜欢他说话的思路敏捷,有跳跃感,但张儿不散,有一种吸引人的表达力。主持人与他的交互也很丝滑,张弛有度。

听他们唠嗑吧,谈笑间大模型AGI的大趋势貌似尽收眼底。还是值得点赞的。

广密条理非常清晰,所涉及的知识面和大形势观非常广泛,却能present到自己的视角参照系,与LLM社区的思想趋势有较好的映射。这不容易,因为LLM这档子事,无论模型还是产业的 landscape,大多都是盲人摸象。很多大专家、商业大佬也都各有自己的三分地和视角,也很难全面评估形势。Meta 小扎刚前不久面对万卡竞赛能不能得到预期的AGI return的天问,回答说(大意):其实没人知道,但总不想万一错过的(典型的 FOMO心态)。

目前形势处于微妙期,其实还没有凝聚太多的共识。但是把英伟达送上天价的几个富可敌国的大佬/大厂,短期内却绝对不允许停止军备竞赛,这是他们科技商业争霸的游戏。这叫欲罢不能,节奏在他们手中。Open AI 不断融资,伊利亚自己也最近融资成功,这些都是这场竞赛持续热度的浪花。

与之相配合的是技术大佬和硅谷AGI主流技术圈对scaling的执着和痴迷。因为这次大模型 scaling 技术浪潮对于资源的无止境需求,真正能有机会实践、感知并做出调整改变路线图的技术人,也只能是一个很小的圈子。

据广密的信息,这个所谓 self-play RL 新生态趋势,目前是局限在硅谷技术大佬小圈子的共识,他提到大约不超过200人的圈子的。如果信息正确的话,一个在硅谷技术核心圈200人以内的某种共识和议论,说明还只是一个动向,甚至连管理圈还没真正 get it 和对齐。

感觉上,广密有一些“春江水暖鸭先知”/“语不惊人死不休”的心态(LOL),有意强调/夸张了这个趋势,警醒国人,甚至说,如果我是大模型创业家,我会200%资源聚焦 RL 方向,bet on it,因为这是未来赢家的选择,云云。

其实,客观说,对于多数人这个不实在,也无可操作性,最多是说给国内大厂玩家或六小龙听的吧,但其实也是白说。RL 本来就不好玩,连开源标杆 Meta Llamma 3 在最基本的 RLHF 方面都选择绕开来走,就更甭提提倡国内大模型公司全力 bet on 以强化学习作为新生态核心的愿景了。何况后者在硅谷最多也只是一种“潜流”,可能要等年底前OpenAI草莓以及Claude新版发布后,才能对这个所谓新生态的影响,看得清楚一些吧。

这个苗头可以 keep in mind,但上半场确实似乎结束了。真正可以在所谓的下半场作为 action items 的,其实不是这种高大上、带有很强探索性的大模型 scaling 方向的尚未确定的 roadmap,更多是趁着 GPT4o/Claude3.5级别的通用模型的推理成本越来越亲民化、趁着LLM供应商多模态功能在进一步推广和完善(例如超拟人全双工语音的最新突破和工具赋能就会大大增加应用层面的机会,还有视频的进展等), 加快找市场切入点(PMF),专注应用场景真正需求的解决。

对于产业而言,当前大模型规模化应用的困局才是悬在大模型产业头上的利剑,决定了这下半场在 tech adoption curve 下行能不能软着陆和最终平缓回升。至于军备竞赛,让马斯克、小扎等首富俱乐部继续玩继续high就好。

作为“预训练”的延深,强化学习属于“后训练”,在前者遇到瓶颈和 diminishing returns的时候,加强后者是自然的补足。从AI对人类认知的模拟来说,前者是继承人类文明的知识和科技积淀,后者是把这些知识真正用到实处,在环境中学习。这个智能学习的总体思路 makes perfect sense,也是大模型应用必须要走的方向。

所以老友吕兄说:“直觉上RL是必须要走的路,因为supervised learning的数据没有那么多了。”

没错,不同程度利用再生数据,其实已经是日常 practice 了,也不再有以前的“心理障碍”,是一个必然。总体而言,模型就是比人能够更高质量产生数据,而且会越来越好。但这还不是这里说的self-play的主动探索和数据再生。

毛老说的也不错:“RL 与人类的认知过程相符,与认识论一致。实质上就是接收外界反馈,接受实践检验的过程。RL 是主动学习,而训练是被动的。”

广密现在是说,需要研究测把 RL 范式化,提供某种 RL dev toolkit,然后有在各种场景去做 scale up RL 的路线。这个所谓“范式大转移”,没有1-2年的大厂/大佬的推动普及,没有抓手。持谨慎乐观或怀疑的open 心态,静观其变吧。

Professor Ma's long paper out

Here is the link to Professor Ma Yi’s presentation from the Shenzhen Entrepreneurship Forum, in Chinese, recommended.

Professor Ma is a compelling speaker, and his talk is definitely worth listening to. His paper on whitebox transformer, over 100 pages long, has just been released (Yi Ma’s white-box transformer paper is available here).  Unfortunately, I haven’t had the time to dig into it yet. We’ll have to wait until more people have accepted or verified it before delving deeper.

His current claims revolve around using an extremely sparse approach to force transparency in transformers, with results that are reportedly on par with BERT and GPT-2 in many benchmarks. However, this doesn’t mean that he will be able to catch up with GPT-3 or later models anytime soon. But to be fair, it’s not a level playing field—he’s an academic without the resources to compete with mainstream AI in an arms race. What he does believe, however, is that he has opened a door—a path toward explainable AI in large models.

Honestly, I’ve always had a litttle bit doubts about Ilya’s theory explanation of shortest program compression (his Berkeley talk). From an ultimate theoretical perspective—where lossless compression is the ideal—the idea of continually scaling training, deepening, and lengthening learning makes sense, as it pushes the model toward becoming the smallest possible program for universal tasks. Ilya’s theory may hold up in this respect, at least in theory or as an end goal. But in any real-world scenario (e.g., under budgetary constraints, with methodological limitations), it’s hard to call a model purely derived through gradient descent the “shortest program,” because these models appear to be gigantic beasts with "huge circuits" inside, intuitively, should not be considered "short or small".

Models with hundreds of billions or even trillions of parameters are massive monstrosities, succeeding mainly through sheer size rather than through high regularity or elegance. Emphasizing how impressive their compression ratios are or how well they handle lossless compression may help explain the generalization and emergeng abilities in sequence learning from a theoretical standpoint. But in practice, any model at a given time is far from being the “shortest program.”

This highlights an unavoidable distance between theory and practice. Ilya essentially hedged practice with theory along a future time axis, but our immediate reality doesn’t seem to align with this. It’s like a clumsy wrestler trying to brand himself as sleek and slender fashion model.  Visually not a fit, to most of our eyes.

Instinctively, LLMs feel full of rote memorization with significant redundancy. Under real-world conditions, achieving extreme or lossless compression seems impossible.

On the other hand, Professor Ma’s sparsity approach almost feels “over the top.” Enforcing the same weight for QKV directly seems a bit crude and simplistic, yet it still managed to be trained successfully. This shows that there’s a lot of flexibility within transformers—no matter what restrictions or pruning are applied, the model still finds a path out. In this sense, Professor Ma’s pursuit of the “shortest program” is more real and direct—it’s so short that even a human can interprete the process (hence the LLM explainability).

Yet the difference between these two extremes is still mind-boggling. On one side, we have gigantic models, and on the other, extreme simplicity to generate whitebox models. The fact that both approaches work is shocking.

Speaking of simplicity and explainability, here’s an interesting anecdote in AI history: Back in the day, during the era of symbolic MT, one of the earliest deployed systems (Siemens' METAL) for English-German translation used only eight symbolic features (such as human, animal, etc.). The rules were simple, transparent, and easy to explain. This shows that extreme simplicity and rule-based transparency can work in some rough application scenarios (where English and German are linguistically close, making translation easier).

Later, we MT-ers expanded the number of features to the thousands, trying to cover more of the long tail. Even then, it wasn’t perfect. At the time, we thought that with enough effort, we could match the quality of statistical MT. But now, we know that even if symbolic MT could catch up and match statistical MT, it’s still far from competing with neural MT.

So, could we have continued refining features further? It wasn’t because we didn’t want to keep extending symbolic features (similar to one-hot encoding, but with the internal structure of ontology/taxonomy). We wanted to go beyond thousands to tens of thousands of features. But in reality, thousands (of features in size) were already reaching the limit of human experts’ capacity to understand (AI explanability), manage and debug. Expanding further would have been unmanageable.

Meanwhile, how many parameters do mainstream Transformer neural networks have? And the space and granularity they represent are on a completely different scale. Given the vast difference in scale between the two, it’s natural to doubt any efforts to bridge this gap for AI explanability.  How could that even be possible?

That’s why I’ve always felt that explainability in large models is an elusive goal. But Professor Ma is telling the world that they’ve achieved it.

 

 

Relevant link:

Professor Ma Claims to Have Fully Unveiled the Mysteries of Neural Networks

What did Ilya see? -- secret behind success of LLMs

马毅教授的演讲,值得一听

创业邦深圳会议马毅教授的演讲链接在此:https://mp.weixin.qq.com/s/ibxGO_A7H-akpbwf2R2mGw

马教授还是很能讲的,他上面的演讲,很值得听。他的100多页论文也已经放出来了,可惜没时间钻研了,等以后更多人接受或验证后再说。

他目前所做出的 claims,是说用那种极度稀疏化的方法逼迫 transformer 透明化,结果也在多方面匹敌了BERT 和 GPT2。但并不说明短期他有办法赶上GPT3以上。话说回来,那也不公平。他作为教授没有资源去以军备竞赛的方式与AI产业主流打擂台。只是说,从路线上说,他觉得自己打开了一扇门,一条可以通向可解释AI的大模型大门。还是应该赞佩这样的反潮流的教授的。

其实,我也一直隐隐约约对伊利亚说的最短程序压缩论,持有怀疑:从终极目的(理论上以无损压缩作为理想目标)来看,不断加大训练、加深加长学习,结果就是朝着让模型真正成为最小程序,伊利亚理论也许没错。但在任何一个实际条件约束下(例如预算约束、方法论约束),这种纯粹靠 gradiant descent “凑出来”的模型/路径,很难说是最小 program,因为模型看上去就是个庞然大物,谈何“最小”。

千亿万亿参数的超大模型本来就是以大取胜,而不是以精简和规则见长的怪兽(gigantic monster),非要强调自己的压缩率厉害,无损压缩做得好,虽然有从理论上方便说明序列学习达成的通用性、泛化现象以及“涌现”能力,但实践中,在任意一个特定时间条件下的模型,都远远不是“最小程序”。

这是理论和实践躲不开的一种矛盾。在伊利亚那里,实际上他是以未来时间轴,用理论对实践做了对冲。我们的真实感觉并非如此,不敢这么说。就好比一个摔跤选手,都那么笨重了,还非要标榜自己性感、苗条?

直觉上,LLM 里面充满了死记硬背和信息冗余的,在现实条件下其实不可能做到极度/无损的压缩。

但另一方面,马教授也太奇了,他的稀疏化直觉上做得“过分”,QKV直接拉平,看上去有点简单粗暴,但居然也最终能训练出来。可见,transformer 的肚子里的操作空间还是很大的,你给它各种限制,动不动就剪枝(化零),也不用担心它走不出来。这种意义上,马教授追求的才是真正的“最短程序”,短到了连“豆腐脑”的人类都可以看懂路径(hence 可解释性)。

疑问还是这两个极端差距太大。一边庞然大物,一边无限精简,二者都能走通,也是震撼了。

谈到精简可解释,谈个掌故。老老年做 symbolic MT,一个著名的早期的实用系统(西门子的 METAL)做英语德语的翻译,整个系统只用了8个 symbolic features(例如人、动物等),规则简单而可解释,系统也一样上线实用了。可见极度精简和规则化,做到完全透明和人类可解释,在粗线条的应用场景(英语和德语距离较近,翻译难度低),有时候也是管用的。

我们后来把 8 个 features 扩展到千数量级,才擦了长尾的屁股。但也没擦干净。当时觉得,也许认真做可以对垒统计MT的品质(与董振东老师谈过,我们都觉得可以在翻译上最终用符号打败统计的,只是需要时间磨细活),但现在知道即便匹敌了统计MT,也远远不能与神经MT比高下。

那就把 features 往细做,成不?不是因为我们不想继续把 symbolic features (类似于 one hot encoding,但人为在 features 内部强加了类似于 HowNet 的 ontology/taxonomy 的结构性),从千这个量级进一步提升到万的量级。实际情况是,千几乎已经达到专家人脑的极限了,再扩大 features 的范围,我们就无法掌控和调试了。

可是,神经里面有多少 params 啊,其所能反映的 representation 的空间和细密度,与千量级的 symbolic features,尺度完全无法比拟。二者表征的尺度如此悬殊,对拉近二者距离的任何努力,我们天然会产生怀疑:这怎么可能做到呢。

所以一直就觉得大模型可解释性是一个可望不可及的目标。马教授告诉世人,他们做到了。

相关链接:

马毅教授称,已经揭开完全揭开神经网络的面纱

NLP老司机的AIGC旅程

今天想到做个小结,以“玩”的心态,回顾一下前两年的AIGC旅程,以及一个NLP老兵一路走来的心路历程和感受。‍‍‍

大模型爆发前,最痴迷的是当时就有的 txt2img 文生图模型。当时尝试过很多种工具,“小雅”就是那个阶段的产物。不仅人物,也做过各种绘画风格,在群里和博客也分享多次。后来疲劳了,就不怎么玩了。

开始对数字人感兴趣,2D 的 talking photo,2.5D 的有姿态虚拟主播,以及 3D 舞蹈等。因为是自家产品「奇妙元」,玩起来没限制,作为“产品体验官”,疯玩了一阵子。

可惜数字人的黄金时期转瞬即去,还没来得及起飞,就开始鱼龙混杂、遍地开花了,市场给卷的。

紧接着对于超拟人/超写实配音,以及跨语言的突破,包括最近“双工”的突破,各大头部模型开始显摆自己的语音亲民能力,与普通真人无异,不再是板着腔调的播音味了。 咱们自家的AIGC拳头产品「魔音工坊」赶上了这波语音tokens化的端到端大模型浪潮,也实现了超写实,那是大约半年前的事儿。意义重大,因为语音是所有copilot类大模型应用的最自然的接口,也是数字人和短视频的必要赋能点,但语音从可玩性上,不如音乐生成。

Suno 惊艳登场,我入迷了几个月,实现了自己也做“音乐人”的梦想。当然,现在也淡化了,不是不好,是没时间玩了。

时间被中国的 Sora,快手可灵AI的视频生成大模型占用了。视频生成疯玩到今天,我用它做了很多儿时的回忆,定格和再现了人生的高光时刻,虚拟了超生活的场面,最 high 的时期也过去了。这一通尝试,包括三分钟视频连续生成的极限试验,以及种种提示词工程探索,对当前视觉大模型的优点短板看得比较清晰了。

视觉模型的重要应用形态之一就是“一键成片”,也是自家产品了,叫「元创岛」。 目前还很粗糙和简陋,但的确做到了“傻瓜”制作能力,零门槛,任何人都可以用它来生成视频。显然有落地场景和起飞的迹象。

这种对多模态体验和迷恋,想起来与一辈子只做文本NLP得经历,本来是格格不入的。但背后有个大模型的宏大背景。原来,LLM炸平了NLP后,马不停蹄,又开始炸平多模态。这种通用性让人觉得这一切有着共同的主线贯之,是自然的技术汇合之流。这是从模型研究的心路历程看。

从人文和科技结合的角度看,我们这种“老文科生”与生俱来对于人文、艺术的追求本性,并没有因为在工业界的码农环境“挖煤”几十年,而(被)湮灭,应用到如今又是一个自然汇聚。这有点像乔布斯当年的说法,他追求的就是人文意味的科技产品,工程结合美学品味,嘲笑微软产品的粗鄙,no taste。

想想这一路走来挺有意思,无论研发还是应用,冥冥之中都在汇聚。而我们何等有幸见证、经历和投入到这种汇聚的潮流中,虽然这个汇聚也同时意味着颠覆自己、碾压自己、否定自己的过往,抛弃很多过去的“绝技”,例如曾经做到世界顶尖的符号解析(symbolic parsing)的庖丁解牛之术。 靠的是终身学习,不至于掉队太远。但一切的一切,更需要一种 精神,尤其是 passion:passion 所驱,乐此不疲。

下一个passion点 应该是 to b 场景,因为最终的应用大期待,大概率在垂直。To c 虽然很卷,但路线图和态势,能做什么,包括 aigc,已经基本清晰。但 to b 还在泥潭里挣扎,方向都还隔雾看花,闪闪烁烁,但也看到高人。例如白硕老师,感觉他就在捻须微笑,坐在金融交易的莲花池上,仗着to b 积淀。

个人而言,垂直赛道,最喜欢是教育,其次是法律,这都在大模型知识能力的路上:既容易最终被通用大模型碾压,又立即能对齐场景呈现价值。金融太繁琐,水更深。水利、电力、汽车等非常专门,行外人感觉枯燥。但医疗和心理,却很诱人,虽然比教育、法律更难涉入。看命运之神领我何往吧。

Decoupling to Resolve: Issue of Character Consistency in Video Generation

I’ve now become the go-to expert for AIGC (AI-generated content) "custom services" among my old friends and classmates, just for fun. Below are nostalgic videos made from old photos that two of my classmates asked me to create.

Whenever I find the time, I’m more than happy to provide this kind of emotional value for friends and family because it’s truly satisfying to see their reactions of surprise.

The pianist is now a world-class piano master, frequently touring and performing in Europe, America, and China. These are precious old photos of him practicing and performing with our mutual friend, Brother Sun, in Philadelphia back in the early days.

Dr. Bai Shuo, a seasoned expert in NLP and a multi-talented musician, commented humorously: “Looks real for someone who pulls on the bow in  Meditation as named, but the bowing and fingering are all wrong.”

Another old friend also left feedback noting that the visual model doesn’t understand music: "This needs improvement! It's obvious that the model was created by someone who doesn’t know how to play the violin or piano. The bowing and piano accompaniment are off. The first note has a two-and-a-half beat long tone, which should be played with a long bow. Additionally, the pianist’s right foot should never be raised or shaking like that—it should be on the sustain pedal.”

LOL

Even though the music's name Meditation was clearly specified in my prompt during generation, there is no model, in the foreseeable future, that can truly align the understanding of music with the intricate details of bodily movements during performance. Perhaps this can be reserved as one of the ultimate challenges for large models aiming for AGI, because theoretically, if enough alignment data of musical performance is available, based on the compression theory of "joint training", it’s possible to aim at perfect alignment across different modalities.

If simulating the objective world is the ultimate goal of visual models, then the current generation of visual models is at the level of “playing the piano to a cow” or “playing music to a tone-deaf audience”—completely unable to withstand scrutiny from musicians. For example, as someone with little musical knowledge, when I watch the nostalgic performance videos above, I wouldn’t notice the flaws as an expert would; instead, I find them vivid and emotionally engaging.

Of course, the standards of musicians might as well just be a "pseudo-demand" or a pseudo-goal (even if the visuals satisfy the picky “expert eye,” so what? Will it sell well?). It might not be worth the effort to pursue this. However, in theory, an ideal AGI should be capable of meeting these expert-level demands.

This is the challenge of musical performance alignment.  Another challenge to Sora-like video generation models is character consistency in videos.

Achieving facial consistency in generative visual models is an extremely difficult. Don’t expect this issue to be resolved by video generation models alone in the short term, especially not through autoregressive methods.

Human eyes are extremely discerning with regards to face recognition, especially when it comes to familiar faces of friends and family—you can immediately tell when a character's appearance is off. For example, while playing with old photos recently, I used the KeLing model (top notch Video Model in China) to generate a video of myself. At the 5-second mark, it still looked passable, but by 10 seconds, it no longer resembled me.

In the second 10-second video, just a slight turn of the head, and it’s no longer me—it looks more like my brother. How can a model handle such fine details? Especially when the starting image for video generation is not even a straightforward frontal shot, making the character information incomplete—how could it not go off track?

While the videos I've made for friends and family using KeLing during its public testing phase have generally been met with passionate surprise and amazement, most of them suffer from this issue of character consistency, which is a regret.

The current one-click video generation products on the market (including our own YuanChuang Island recently launched) tend to mainly use anime or manga styles. This is to avoid user scrutiny since these styles lack 3D distinct individual characteristics. As long as there is consistency in attire, no gender mix-ups, with age and race alignment, most people will accept it. The current one-click videos are generally rough, with entertainment value primarily in the story rather than character portrayal akin to a Hollywood blockbuster. However, as this path progresses, it will inevitably encounter the challenge of maintaining the consistency of digital IP actors and their roles.

My colleague, Lu, mentioned, "the consistency issue might require cross-checking from multiple video angles, which more or less touches on the core issue of whether modeling is necessary."

Indeed, some form of cross-checking is required, not just monotonic correction over time/sequence—that is indeed the key. There’s a need to decouple or separate the character's image from the storyline, rather than generating in a linear, one-way path. While sequence learning has indeed produced miracles in LLMs, sequence generation inherently has limitations, including random deviations over time. Although it's not as extreme as LeCun's criticism—where he says GPT's error accumulation is a tiny discrepancy that leads to a significant miss—his claim isn't entirely accurate because GPT's autoregressive operation also corrects and adjusts its course at every step in the context. Nevertheless, when it comes to fine-grained consistency, random deviations are almost impossible to handle, even with corrective mechanisms in place.

Hence decoupling, decoupling, decoupling! Decoupling can solve the problem. The world isn't limited to sequences. Beyond sequences and time, there is a constant abstraction (i.e., character image, or IP) that can be utilized. This is becoming increasingly clear. Take, for example, the digital IP character Maria (Xiao Ya) that I created using AIGC txt2img more than 2 years ago::

Unless they’re fans, perhaps my numerous Maria videos might cause aesthetic fatigue—someone even called her “Dr. Li's fairy” (LOL). But indeed, there are fans; several of my old classmates are among them.

Why? Because she is an IP, and she has been decoupled.

 

Related Links (original posts in Chinese):

视觉模型生成的极限对齐

解耦才能解套:再谈视频中的人物一致性问题

 

解耦才能解套:再谈视频中的人物一致性问题

前两天说过,对于生成式视觉大模型,人脸一致性是个非常难缠的东西,不要指望短期靠视频生成大模型本身来解决,尤其是不能指望自回归解决这个问题。

人眼太挑剔了,尤其是亲友和熟人,你会一眼看出人物走形了。譬如这几天玩老照片,我用头部视觉模型可灵5秒生成我自己,还过得去,到了10秒就不是我了。

10秒视频:

一转脸就不是我了,倒更像我哥。这种细粒度,模型怎么能搞定。尤其是,如果图生视频的起点图就不是正面照,character 信息本就不全,怎么可能不走偏。所以,我在可灵公测以来为亲友同学做的视频虽然普遍感觉惊喜或震撼,但大多存在这个人物变形的问题,成为一个遗憾。

现在市面上做的一键成片产品(包括我们的元创岛),其所以用二次元或其他夸张风格为主,是为了避免用户的挑剔,因为那些形象缺乏鲜明的个性,不是真正的 individual IP,只要保持穿戴一致性,男女不要错位,年龄和种族不要相左,一般人也就接受了。目前的一键成片普遍比较粗线条,娱乐价值更多是为视频里的故事,而不是好莱坞大片那样的角色形象刻画。但这条路往上走,就躲不开这种 digital IP 的演员角色定位及其一致性问题。

吕兄说:一致性问题可能需要靠多角度视频的cross-checking, 这里面多多少少要涉及到是不是要建模的硬核问题。

是的,要某种 cross-checking,而不是时间流单调矫正,这是key。需要解耦/剥离故事线上的人物形象,不能生成一条道走到黑。序列出过奇迹,但序列生成有随着时间出现随机偏差的局限,虽然不是 LeCun 批评的那样极端:他说gpt错误积累是差之毫厘失之千里;他的说法不正确,因为gpt的“自回归”推理方式也在每一步context自回归中不断纠错、矫正航向。尽管如此,对于细线条一致性,随机偏差哪怕有了矫正机制,也是基本搞不定的。

因此,解耦、解耦、解耦。解耦就可以解套。世界上也不是只有序列。跳出序列和时间,还有个恒定抽象(即character形象)可以利用。这一点已经越来越清晰了。以我制作的数字人IP形象小雅/Maria为例:

除非粉丝,也许我的众多小雅视频会引起审美疲劳吧,有人称她为“立委的妖精”(LOL)。但确实有粉丝,老同学中好几位人物就粉她。

为啥,因为她是IP,解耦了。

Related Links:

视觉模型生成的极限对齐

马毅教授称,已经揭开完全揭开神经网络的面纱

原创 立委 LiWeiNLP 2024年09月01日 17:44 北京

马教授的白盒transformer论文在:https://ma-lab-berkeley.github.io/CRATE/?continueFlag=680deb7516c156566f8eb73fdcc896ca

马毅教授大名鼎鼎,特立独行,一方旗帜人物,天下无人不识君。值得关注的是他最近对主流大模型的批评以及他自己工作的宣示。

前不久深圳会议请他来,他把主流大模型、伊利亚,以及k氏复杂性理论,批为中学生的水平,说他们根本不懂理论。而他自称理论实践双突破,说的就是他带领团队做的这个白箱 Transformer,不仅揭开了大模型的神秘面纱,而且是工程可实现的替代方案。

说句实话,把话说到这份上的人,多半是真牛人,有自带的底气和傲视群雄的霸气。对于主流的批判,老美有个杨立昆(他说GPT不如他家一条狗,是死路,他的世界模型才是替代方案),中华有个马教授,世界才显得不那么寂寞。也确实希望他们somehow能弥补当前主流大模型的“慢思维短板”,推动AI的整体进步。有时间还是应该慢慢研究他的学术和实践,但也许更多是要等待时间和同行对他们工作的检验和复现。

深圳会上就露了个脸,自己讲完和批完,立马走人。估计是真忙。

论文100多页,说过几天就放出来。看现在的 outline,重点在,quote:

Overall, CRATE is similar to a transformer, with two differences:

in each attention head, the Q,K, and V weight matrices are weight-tied, i.e., set to be equal;

and the nonlinearity following each attention layer is no longer a multi-layer perceptron (MLP), but rather a more structured operator (ISTA) with sparse outputs.

咱们了解一下,ISTA(Iterative Soft-Thresholding Algorithm,迭代软阈值算法),是一种用于解决稀疏优化问题的算法,在机器学习领域有广泛应用。在CRATE架构中, ISTA被用来替代传统Transformer中的多层感知器(MLP),还记得前不久的 KAN 的创新也是旨在平替 MLP。都是在 Transformer 里面开刀。

我浅薄的理解,ISTA 与 KAN for Science/Physics 的思路是一致的,就是要经过某种正则化或剪枝,最终拟合成稀疏化路径,从而获得可解释性。

工作原理: ISTA通过迭代的方式逐步接近问题的最优解。每次迭代包括两个步骤: a) 梯度下降步骤,这与主流同;b) 软阈值操作。增加这个操作,是要在两个目标之间找平衡:

a) 使模型尽可能准确;b) 使模型尽可能稀疏,即简单(因为人很难理解一团乱麻)。

软阈值操作鼓励内部元素变为0,从而产生稀疏的输出,增加可解释性。权重绑定的注意力机制和ISTA(迭代软阈值算法)促进了模型对输入数据结构的更深入理解。这更像人类的结构化解析过程,抓大放小,正则化、规整化。

马教授说的是,它们经过上述的两个改造,结果在监督学习中(后来他们也成功地用到了自监督学习),就自然学到了人类的这种结构化、稀疏化所带来的可解释性。

拿图像识别来说,研究发现某些注意力头对应于动物的不同部位。更引人注目的是,这种对应关系在不同动物甚至不同动物类别之间保持一致。例如,专注于"头部"的注意力头在处理不同种类的动物时,始终关注其头部区域。这种一致性表明CRATE已经学习到了跨类别的通用视觉特征表示。

但做LLM可解释性研究的人早就发现,在MLP网络的尽头,各种结构化(例如 头、脚)的部件也都被注意力抓到了,否则不能解释LLM表现出来的泛化(或压缩)能力。困难的是在网络的前期,那些个注意力比较混杂,主流的MLP搞不清都在注意什么。隐隐约约是在注意像素(点)、线等基础元素之间的关系。

可解释AI的基本思路都是一样的,就是要把一团麻一样的黑箱多层网络内部数据拟合中凑出来的路径,改造成强加了种种限制条件和剪枝、化零的“结构化”路径。

正常人,谁不愿意模型可解释?所以这种稀疏精简的路数/算法一定也是有极大的挑战,才 so far 无法与那种黑箱子瞎凑的方法比试。

马教授觉得自己很牛的底气是因为他在最近半年一年,开始用可解释的上述白箱方法,训练出了可以匹敌传统 transformer 的成绩。他在深圳会上说,此前他有信心这才是正道,但在没有拿到结果前,还是必须谨慎。现如今,他觉得他的跨国团队用这一套在各方面所做的实现,已经让他满意到可以向全世界宣告,他找到了理论和实践的突破口,找到了transformer白箱化的正确方法,可望引发范式转变式的深度学习的突破。这令他很激动,也很霸气。所以他不再满足于大学教授的理论建树,而是觉得应该下场了。马教授自己成立了一家公司要在工程上做这个推进。这次来创业邦深圳主题宣讲,据他说,是他第一次以这个新公司的名义来向社会宣布这个挑战主流的工程落地的方向性大项目。

不过,凭着多年经验和直觉说一句这件事的挑战性(或可疑之处):人类的可解释性建立在一个非常小的高度精简的 finite set 基础之上。如果拿 symbolic features 来说,超过千这个量级的 feature system,对于人就是不可解了。贵为上帝选民,我们人类的“豆腐脑”真地很局限。可是另一边看 transformer 里面的 parameters 以及 attention heads 的KQV数量,完全不具有可比性,天上地下。让大变小在这样的尺度下去归约,感觉上是不可思议的。

KAN for Science 之所以成功,是因为他们瞄准的 target 极为狭窄,只是 science 中的某些已有的解析公式,或者未知的但局限在少数参数的潜在公式。有了这样的目标去做剪枝,中间加上科学家的人为干涉或反馈,KAN 声称是做到了可解释性。

Anyway,马教授似乎是成竹在胸,咱们还是静观他的法术/魔术。

Related Links:

What did Ilya see? -- secret behind success of LLMs

 

 

Professor Ma Claims to Have Fully Unveiled the Mysteries of Neural Networks

Professor Yi Ma’s white-box transformer paper is available here.

Professor Ma is a prominent figure, renowned for his distinctive style and leadership in the field. His name is widely recognized and respected. Of particular interest recently are his critiques of mainstream large models and the bold claims he has made about his own work (see his post in Chinese below).

Recently, at a conference in Shenzhen (which I attended with my own talk too), Professor Ma sharply criticized mainstream large models, Ilya, and Kolmogorov complexity theory, dismissing them as being on the level of high school students and claiming that they lack a true understanding of theoretical concepts. He asserted that he has achieved breakthroughs in both theory and practice, particularly with the white-box Transformer developed by his team. According to him, this model not only demystifies the complexity of large models but also offers an engineering-feasible alternative.

When someone speaks with such confidence, it usually indicates genuine expertise and a commanding presence. Just as Yann LeCun in the U.S. criticized GPT as being inferior to a dog and called it a dead end, proposing his world model as an alternative, China has Professor Ma. Their critiques balance the global discourse, making the world feel less excluding. There is indeed hope that their work might address the "slow thinking" and "interpretability" shortcomings of current mainstream large models and contribute to the overall advancement of AI. Professor Ma’s academic and practical work deserves close study, though we may have to wait for time and peer reviews to fully test and validate their findings.

At the Shenzhen conference, after delivering his talk and sharp critiques, Professor Ma left immediately, likely due to his busy schedule.

The paper is over 100 pages long and is said to be released in a few days. Based on the current outline, the key points are as follows:

Overall, CRATE is similar to a transformer, with two differences:

- In each attention head, the Q, K, and V weight matrices are tied, i.e., set to be equal.
- The nonlinearity following each attention layer is no longer a multi-layer perceptron (MLP) but rather a more structured operator (ISTA) with sparse outputs.

Let's examine ISTA (Iterative Soft-Thresholding Algorithm), a widely used algorithm for solving sparse optimization problems in machine learning. In his  CRATE architecture, ISTA replaces the traditional MLP in Transformers. Not long ago, KAN also introduced innovations aimed at replacing the MLP, both approaches representing surgeries within the Transformer architecture.

In my understanding, ISTA and KAN (for Science/Physics) share a common goal: through regularization or pruning, they ultimately fit a sparse path, thus achieving interpretability.

How it works

ISTA Iteratively approaches the optimal solution of a problem. Each iteration involves two steps: a) a gradient descent step, which aligns with mainstream methods; and b) a soft-thresholding operation. This operation is added to balance two objectives:

a) Maximizing model accuracy;
b) Achieving model sparsity, i.e., simplicity (as overly complex models are difficult for humans to interpret).

The soft-thresholding operation encourages internal elements to become zero, resulting in sparse outputs and increased interpretability. The weight-tied attention mechanism, combined with ISTA, promotes a deeper understanding of the input data structure, resembling a human-like structured analysis process that prioritizes key elements while regularizing the data.

Professor Ma claims that these two modifications naturally lead the model to learn the interpretability associated with human-like structuring and sparsity during supervised learning (and later as claimed successfully applied to self-supervised learning too).

For example, in image recognition, it was observed that certain attention heads correspond to different parts of animals. What's more remarkable is that this correspondence remains consistent across different animals and even different categories of animals. For instance, an attention head focused on the "head" consistently pays attention to the head area when processing different kinds of animals. This consistency suggests that CRATE has learned a general representation of visual features across categories.

However, those studying LLM interpretability have long discovered that at the end of MLP networks, various structured components (such as heads and feet) are also captured by attention mechanisms. Without this, it would be difficult to explain the generalization (or compression) capabilities exhibited by LLMs. The challenge lies in the early stages of the MLP network, where attention is more mixed, and mainstream researcher struggle to clarify what the attentions heads are focusing on. It seems that they are vaguely paying attention to the relationships between basic elements like pixels/dots and lines.

The core idea behind explainable AI is consistent: transforming the tangled, black-box, multi-layer network's internal data fitting paths into structured paths that are enabled with various constraints and pruning, leading to a sparse representation.

Who wouldn’t want a model to be interpretable? However, achieving sparsity and simplicity is extremely challenging, which is why, so far, these approaches have struggled to compete with the black-box methods that involve randomness.

Professor Ma’s confidence stems from the fact that, in the past six months to a year, he has begun to train models using the explainable white-box methods mentioned above, achieving results comparable to traditional transformers. At the Shenzhen conference, he mentioned that while he had always been confident that this was the correct approach, he remained cautious until results were obtained. Now, he believes that his cross-national team’s achievements with this approach have satisfied him enough to announce to the world that he has found a breakthrough in theory as well as practice, the correct method for white-boxing transformers, which could lead to a paradigm shift and a breakthrough in deep learning. This has made him both excited and confident. Therefore, he is no longer content with academic theoretical achievements alone; he feels compelled to take actions in industry as well. Professor Ma has recently founded a company to advance this work on an engineering level. At Shenzhen, he announced a directionally significant project challenging the mainstream, first time under the banner of his new company.

However, based on my years of NLP experience and intuition, I must point out a challenge (or potential issue): Human interpretability is built on a highly simplified finite set. If we consider symbolic features, a feature system with more than thousands of elements becomes incomprehensible to humans. But on the other hand, the number of parameters in transformers and the number of KQVs for attention heads are on a completely different scale. Reducing such complexity on this scale seems almost unimaginable.

KAN for Science succeeded because their target was extremely narrow—certain existing symbolic formulas in physics or potential formulas limited to a few parameters. With such a goal, pruning, along with scientist intervention or feedback, allowed KAN to claim interpretability.

Regardless, Professor Ma seems confident, so we would like to observe how his methods and results evolve and will, or will not, be accepted.

 

 

Related Links:

What did Ilya see? -- secret behind success of LLMs

 

创业邦深圳会议演讲笔记:推动AIGC商业落地,出门问问的「产模结合」实践

8月22日,2024AGI商业趋势大会在深圳成功举办。活动由创业邦主办,南山区科技创新局、西丽湖国际科教城融合创新中心、深圳市“模力营”AI生态社区为支持单位,揽胜运动为大会指定用车,搜狐为战略合作媒体。

本届大会以“AI塑造未来市场”为主题,邀请人工智能领域的行业专家、上市公司、独角兽企业、创新企业、投资机构代表,分享研讨最新的技术、创投、治理热点话题,并围绕产品商业化、人工智能+行业解决方案进行深度交流,共同探寻更广泛的产业合作与创新机遇。

会上,出门问问副总裁李维在主题为“盈利为王,AIGC从技术创新走向商业落地”的演讲中精彩观点如下:

  1. 大模型的出现,就像翻越了语言的大山,统一了这些不同的赛道,建立起了一种类似圣经中巴别塔的通用语言能力。
  2. 历史上,技术革新往往遵循一条被称为“technology adoption curve”的路径:一开始,人们对于重大创新和突破趋之若鹜,但当这些创新在商业化、盈利和实际应用方面遇到瓶颈时,就会出现一段回落期。
  3. 在通用人工智能时代,一个模型可以处理各种任务,这使得许多细分领域的创新空间被压缩。以前,每个细分赛道都有机会诞生超级应用,但现在这种可能性大大降低了。

以下为演讲内容,由创业邦整理:

很高兴有机会跟大家分享我们在AIGC赛道上的一些工作。我叫李维,是出门问问的VP。我今天演讲的题目叫“盈利为王,AIGC从技术创新走向商业落地”。

实际上这个题目挺难的,原因在于AI和大模型非常热闹,但是真正走向商业落地的寥若晨星。所谓“AI一日人间一年”,大模型的发展虽然很快,但从总的趋势上来看,AI实则进入了一段下行期。历史上,技术革新往往遵循一条被称为“technology adoption curve”的路径:一开始,人们对于重大创新和突破趋之若鹜,但当这些创新在商业化、盈利和实际应用方面遇到瓶颈时,就会出现一段回落期。我们现在正处于这一回落下行期,但尚未触底。

这一趋势也映射到AI投资上,今年对于大模型公司来说,融资变得极具挑战性。主要原因是大模型非常烧钱,算力消耗往往远超人力成本。然而,只投入不产出的模式是不可持续的。尽管大模型拥有巨大的潜力,但要实现盈利并不像看起来那么简单。

大家都在谈,上一次技术革命是移动互联网,虽然它的技术含量可能不如大模型带给人的震撼,但它确实催生了一系列超级应用,很好地满足了人们“吃穿住行、娱乐和通信”等基本生活需求。这些超级应用已经在上一代技术革命中达到了很高的水平,留给后来人创新提升的空间已经不多。想要通过大模型在这些领域实现进一步的突破,变得非常难。

ChatGPT的出现标志着通用人工智能的一次大爆发。在通用人工智能时代,一个模型可以处理各种任务,这使得许多细分领域的创新空间被压缩。以前,每个细分赛道都有机会诞生超级应用,但现在这种可能性大大降低了。

我举个例子,机器翻译并不是大模型时代的新现象,早在12年前神经网络革命开始时,机器翻译就是首批受到影响的领域。结果,像百度、谷歌这样的大公司都把机器翻译服务做成了免费产品。一旦一个领域变成了免费或白菜价,创业的空间就大大减少,甚至可能扼杀整个产业。类似的情况也出现在大模型普及之后,比如Jasper。在ChatGPT出现之前,大模型刚刚兴起,那时业内人都知道GPT3。Jasper利用大模型的能力,将其包装成可以帮助人们进行写作和生成各种文案的工具,吸引了一批客户。

随着ChatGPT等通用大模型的崛起,Jasper的优势逐渐消失,开始走向衰落。ChatGPT不仅仅是一个超级应用,它通过人机对话的方式,实际上已经成为一个“超级的超级应用”(super super-apps),它超越了传统的界限。通用大模型现在能够处理各种语言、知识,甚至多模态的内容,如语音、音乐、图像和视频。这种广泛的能力使得通用大模型在很多领域都占据了主导地位,挤压了相关赛道的生存空间。

如今,作为投资人或创业者,在寻找发展方向时,需要经常思考一个问题:我现在所在的领域或开发的App是否具有持久的竞争优势?虽然可能暂时吸引一些客户并满足部分需求,但如果我的项目挡在这个大模型自然扩散能力的路上,那么我最终很可能面临像Jasper那样的衰退命运。

通用大模型的能力如此之强,被寄予很高的期望,结果在真正落地应用的时候,才发现原来有很多限制条件在。一方面,上一代技术已经相当成熟,吃穿住行等低枝果实的超级应用已经做到极致;另一方面,大模型本身还在不断发展,其通用能力常常限制了落地赛道的发展空间。因此,大家普遍发现将大模型规模化落地应用是一件相当困难的事情。

我曾是大模型的热烈支持者,也是在中国最早“鼓吹”大模型的人之一。当时非常激动,因为在我一辈子的NLP职业生涯中,从未见过如此革命性的变化。

在NLP领域,过去我们有机器翻译、对话系统、问答系统等多个专业方向,甚至还有分词这样的细分技术。但大模型的出现,就像翻越了语言的大山,统一了这些不同的赛道,建立起了一种类似圣经中巴别塔的通用语言能力。大模型的出现,彻底改变了NLP行业的格局。但实际应用起来,我们发现它比我一年多前想象的要困难得多。例如,以NLP为方向的应用(如各种文案或翻译的 co-pilot)因为已经被头部大模型搞定,这个方向的创业产业就做死了。

现在,大家都在期待大模型原生的超级应用(LLM-native Super APPs),虽然呼声很高,行业内竞争激烈,但真正能够规模化落地的,目前还只有像ChatGPT、豆包、文心一言这样的通用类的toC应用。

简而言之,这种超级大模型正逐步深入社会的各个层面。然而,除了它之外,我们还没有看到其他大模型实现规模化落地的成功标杆案例,大家都在艰难跋涉,试图找到与通用大模型基本能力有差异化的突破口和切入点。

目前的情况是,无论是行业内外人士、投资者还是创业者,都对大模型的技术潜力广泛认可,这仍然是基本共识,但要找到它的market fit并实现规模化落地,可能需要至少两三年的时间探索:现在处于技术落地的难产期,也是突破期。

大模型虽然功能强大,但也存在一些严重的短板:第一,信息准确性问题:由于学习了大量信息,大模型可能在记忆不精确的细节时出现错误,导致其输出内容不准确,所谓“幻觉”。第二,可控性问题:与以往的符号逻辑AI不同,大模型包含百亿千亿参数,运作起来像一个巨大的黑箱,难以进行精确的调试和控制。第三,安全性问题:大模型可能存在安全隐患,公开上线需要谨慎。第四,算力成本问题:尽管随着技术进步,算力成本有望降低,但与上一代边际成本趋于零的许多应用相比,使用大模型的应用在算力成本上仍然相当高。推理时也常常遭遇高并发的瓶颈。普及到 toC的大众市场,巨大的推理成本直接影响盈利能力。

以上的分析回顾,听起来有些悲观。但长远一点来看,特别是在接下来的三到五年内,还是值得期待的,尤其是垂直赛道(尽管垂直领域尚未迎来大爆发,但许多人在积极探索)。今天上午座谈会的嘉宾们也在分享他们如何将大模型技术应用到自己的领域,都是从需求出发的第一线分享。这些努力最终将取得成效,预计在未来几年内,垂直领域将会迎来显著的发展和突破。

大模型的研发成本高昂,这对创业公司来说是个挑战,而且模型与产品之间的脱节也是一个主要问题。我们目前正在探讨如何将模型与产品有效结合,以克服这些困难。接下来,我将分享我们在这方面的探索和经验。

模型开发可以选择自主研发或使用第三方服务,这取决于产品的具体需求。目前市场上竞争激烈,第三方服务往往价格低廉,更具成本效益。但如果产品需要高并发处理能力或有特定的定制化需求,第三方服务可能无法满足,这时自主研发就显得重要。

这就需要探索如何将模型开发与产品需求紧密结合,以实现成本控制和产品定制的灵活性。这包括调整大模型以适应我们的产品需求,实现最优的产品模型结合。

然而,产模结合说起来容易做起来难。因为模型开发和产品开发是两个不同的领域,涉及的人员背景和思维方式各异。要让这两类人员有效协作,发挥出最大的协同效应,对许多公司来说都是一大挑战。

我们开发了自己的大模型,名为"序列猴子",基于此,我们推出了多模态AIGC产品,涵盖数字人、配音和短视频一键生成等功能。此外,我们还成功推出了出海产品"DupDub "。"魔音工坊"是我们在市场中占据主导地位的产品,尤其在抖音平台上,约70%的声音内容都使用了我们的技术。

我们的目标客户群体包括内容创作者(ToPC,to professional consumer)和小型至中型企业(ToSMB,to small medium businesses)。内容创作者愿意为方便他们工作的工具付费,而我们正是为他们提供这样的工具。对于ToB客户,我们专注于为中小企业提供较为标准化的解决方案,因为大型客户的定制化需求较为复杂,不易操作。目前,我们拥有86万付费用户,这证明了我们的服务已经成功落地并得到市场的认可。下面是我们产品的一些展示。

转述老领导的硅谷风投现状和展望的分享

摘要

1. 未来几年内 65% 的风险投资公司将倒闭。

2. 凭借 2010 年代的出色回报,风险投资一路高歌猛进。

3. 2021 年,1577 家不同的风险投资公司共筹集了 1830 亿美元。

4. 但与此同时,创办一家初创公司的成本却越来越低。广泛可用的工具、全球劳动力和便捷的(在线)分销意味着创办一家 SaaS 公司从未如此简单或便宜。

5. IPO 窗口关闭了——公司无法上市。因此,风险投资公司无法通过大型 IPO 赚钱。

6. 并购也没有发生(至少对卖方来说价格不高)。因此,风险投资公司无法通过出售公司来赚钱。

7. “我们决定不再筹集另一只基金。” 翻译:他们可能做不到。更多的公司会说他们“不再投资”;合伙人“决定担任运营职务”。董事总经理正在退休。

8. 2023 年,597 家风险投资公司筹集了 810 亿美元。这分别下降了 63% 和 56%(与 2021 年相比)。

9. 风险投资的狂欢派对结束了。或者至少这一章结束了......

10. 超过 50% 的现有公司将无法生存。这意味着,如果您是在这种环境下筹集资金的初创公司首席执行官或运营商,您需要了解游戏规则已经改变。

11. 不要相信一些创始人仅凭 20 万美元的 ARR 和一份好牌就筹集了 3000 万美元的故事。风险投资公司用巨额支票救助经营不善的企业的时代已经结束。

12. 筹集资金的最佳时机是您不需要它的时候。深入挖掘以度过冬天。

以上是我的“老领导” Jonathan前天在LinkedIN关于硅谷风投下行的一种描述。很清醒。

听上去似乎奇怪,大模型爆发以后很快风投成为下行,这其实还是因为 technology adoption curve 本身现在在落地应用上遭遇挑战,整体处于下行,无论中美。

大模型的到来使得初创的启动变得容易,这样僧多粥少,筹集风投就更加困难。

我这个老板一共做过4家初创,第一家是他初出茅庐时候做的 Netbase,我们相处非常好。现在这第四家SAAS创业很扎实,势头很好,有望成为硅谷独角兽。他也历练成熟多了。

他是第一个在LinkedIn上给我写推荐的人,一直保持联系。

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Jonathan Spier

Last Friday, I had dinner with a famous VC investor who told me 65% of VC’s will go out of business in the next few years. I believe him. Here’s what that means for startup leaders waiting on their Series A/B/C:

Those of us in startups tend to think VCs are at the top of the food chain.

They aren’t.

VCs are businesses too.

They raise money from THEIR investors (aka LPs).

And their job is to make a return for those LPs.

With outstanding returns in the 2010s, VC was on a win streak.

Many more funds were born.

And existing funds got much larger.

In 2021, 1577 different VC firms raised a total of $183 billion.

But at the same time, costs to launch a startup have gotten cheaper.

Widely available tools, global workforce, and easy (online) distribution mean it’s never been easier, or cheaper, to start a SaaS company.

So how are VCs supposed to deploy all that money they’ve raised?

They can’t.

There is too much money chasing too few deals.

Make no mistake, for VCs, it’s a fatal mix.

The IPO window is closed – companies can’t go public.

So VCs aren’t making money with big IPOs.

M&A isn’t happening (at least not at good prices for sellers).

So VCs aren’t making money by selling their companies.

If VCs aren’t making money, they can’t return capital to their LPs.

They are in trouble.

Of course, VCs rarely go out of business the way their companies might.

Reputations are at stake, so change happens quietly.

But it’s the same result.

It’s already happening.

Listen carefully, and you’ll hear VCs saying:

"We have decided not to raise another fund.”

Translation: they probably can’t.

More firms will say that they are “no longer investing”

Partners are “deciding to take operating roles.”

Managing Directors are retiring.

In 2023, 597 VC firms raised $81B.

That’s down 63% and 56% (vs. 2021).

The VC party is over.

Or at least this chapter is...

The select few at the top of the VC list will have their pick of deals.

The great business builders will choose their spots and continue to thrive.

I’ve been lucky to work with a few of those and am certain that their expertise and relationships will carry them through.

But over 50% of existing firms, won’t survive.

That means if you're a startup CEO or operator raising money in this environment, you need to understand the game has changed.

Don’t buy the stories of some founder that raised $30M with $200k ARR and a good deck.

The era of VCs bailing out bad businesses with huge checks is over.

Many of the VCs won’t even be around in a few years.

There is only one strategy that works in this economy.

Focus.

Nail your ICP.

Delight your customers.

Get profitable to control your financial destiny.

The best time to raise money is when you don’t need it.

It’s a harsh economy out there.

Burrow down deep to survive the winter.

Remember:

Tough markets make strong companies.

视觉模型生成的极限对齐

我现在成了老友同学中的AIGC定制服务的大师了,下面是两位老同学请我做的AIGC老照片怀旧视频。我只要能抽出空,非常乐于为亲友提供这种情绪价值,因为很开心看到他们的惊喜。

现在刘老师可是世界级钢琴大师,常在欧美中巡回演出。这是当年与我老同学孙兄在费城一起演奏练习的珍贵老照片。

吹拉弹唱无所不能的NLP老司机白硕老师评论说:会拉沉思的表示,弓法指法都不对。前微软NLP老友吴兄也留言反映视觉模型不懂音乐:“這個有待改進哦!一看就是不會拉小提琴和彈鋼琴的人做的/:,@P。音樂和弓子的運行差太多,第一個音是有兩拍半的長音,應該給個長弓才對, 另外鋼琴伴奏的右腳永遠不會這樣翹著,或抖動著。他的右腳應該在 sustain pedal 上。”

LOL

生成的时候 虽然prompt里是标明了音乐的名字的,但在可见的将来,没有哪家模型能够真正做到音乐理解与演奏肢体动作细节的对齐。或可留作大模型AGI的极限挑战题(之一)吧,因为理论上如果有足够的对齐数据,根据联合训练的压缩理论,做到极致不同模态之间是可以对齐的。

如果客观世界模拟器/仿真器是视觉大模型的最终目标的话,当代的视觉大模型处于“对牛弹琴”和“对音乐盲弹琴”的段位,绝对经不起音乐家的检验。譬如,我这样的乐盲,看上面的怀旧演奏视频,就不会像音乐行家那样一眼看出破绽,反而觉得惟妙惟肖,情绪拉满。

当然,音乐家的标准也许就是个伪需求伪目标(让挑剔的“专家眼”满意了视觉细节又如何?能大卖么),也许并不真值得费力追求。但理论上,理想的 AGI 都应该搞定这些专家水平的要求。

 

 

立委论LLM:什么是AI刚需

什么是刚需?

 

朋友圈看到一个鸡汤:“穷人说,有钱就是幸福;盲人说,能看见就是幸福;乞丐说,有饭吃就是幸福;病人说,能活着就是幸福;单身说,有伴侣就是幸福。...". 可以诠释用户刚需。

让穷人富起来是刚需,当然很多时候,因为它是刚需,不乏人类饥不择食的本性,刚需者成为被割的韭菜。

吃饱肚子是致富的子集,是最刚的需,因此AGI路上最应该实现的是UBI,不能让资本家独享技术进步和裁员的红利。

让盲人重见光明是让病人痊愈的刚需之最,二者说明,医疗无论多么艰难,仍然是最诱人的垂直领域。

以上属于吃穿住行生老病死的低层次刚需。

但最后,“脱单”却是高低层次融合(物理和精神陪伴:原始性吸引+情绪价值)的刚需。这个刚需恰好是 LLM native 的 sweet spot,不会因为 character.ai 商业化暂时受挫而失去前途。

以前说过,能让一个人感觉成功脱单,对提供商是功德,对用户是砸锅卖铁也心甘情愿的生活品质提升:绝对有付费意愿。

(也许是年轻脆弱多愁善感吧,孤独飘零在大北京,形单影只,当时既没有互联网,也没有游戏,生活中也见不到找到可心女孩的希望。在脱单前记得年轻做过多次噩梦,以为一辈子要打光棍了,不寒而栗,刻骨铭心。)

长期看,老年陪护、心理therapy等都属于 LLM native 的刚需,找到切入点,排除万难做到底做好的赢家,不仅是赚钱,而且是功德。

这些应该属于第一性原则的思考。