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Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens
- Gusarich 1y agoThe article already seems outdated on the first day. The key points about SFT are irrelevant in the era of RL.
- acosmism 1y agoremind me in 2 days
- floppiplopp 1y ago'Chain-of-thought AI "degrades significantly" when asked to generalize beyond training.' - yeah thanks Captain Obvious.
- NitpickLawyer 1y ago> Without specification, we employ a decoder-only language model GPT2 (Radford et al., 2019) with a configuration of 4 layers, 32 hidden dimensions, and 4 attention heads. Yeah, ok. The research is interesting, warranted, but writing an article about it, and leading with the conclusions gathered from toy models and implying this generalises to production LLMs is useless. We've been here before with small models. Training on LLM outputs leads to catastrophic collapse. Every outlet led with this. But no-one red the fine-print, they were testing on small toy models, and were using everything that came out to re-train. Of course it's gonna fail. L3 / phi / gpt-oss models showed that you can absolutely train on synthetic datasets and have great results. Research in this area is good, and needed. Mainly to understand limitations, discover if there are any scale levels where "emergent" stuff appears and so on. But writing articles based on incipient research, based on tiny models is not worth the effort.
- suddenlybananas 1y ago>Training on LLM outputs leads to catastrophic collapse. Every outlet led with this. But no-one red the fine-print, they were testing on small toy models, and were using everything that came out to re-train. Of course it's gonna fail. L3 / phi / gpt-oss models showed that you can absolutely train on synthetic datasets and have great results You're conflating two very different things. Training on synthetic data one time is very different than cyclically training models on their own data. It has nothing to do with model size.
- tankenmate 1y ago"Training on synthetic data one time is very different than cyclically training models on their own data.", but every one with even a modicum of understanding of feedback knows that cyclic training on its own output will end in tears; it's bordering on a tautologic inverse.
- bee_rider 1y agoIs there an actual general principle or theorem or anything that you can link on this? I’m skeptical because these “model collapse” ideas sound vaguely technical and intuitive, but mostly seem to be based on observations about things that happened to happen with current LLMs. It gets bandied about like it is the most obvious thing, but the support mostly seems to be… pseudo-technical vibes.
- NitpickLawyer 1y agoPerhaps I worded it poorly. My main point was that articles focus on the wrong thing. Most coverage of that paper was "Using LLM generated data leads to CATASTROPHIC collapse". Without reading the fineprint. > [...] cyclically training models on their own data. It has nothing to do with model size. Of course it does. GRPO is basically "training models on their own data". You sample, you check for a known truth, you adapt the weights. Repeat. And before GRPO there was RLAIF which showed improving scores at 3 "stages" of generate - select - re-train. With diminishing returns after 3 stages, but no catastrophic collapse. My main point was about articles and cherrypicking catchy phrases, not criticising research. We need the research. But we also need good articles that aren't written just for the negativity sells titles. cheeky edit: see this thread [1]. I know slashdot has fallen a lot in the last years, but I skimmed the root comments. Not one addressing the "toy" model problem. Everyone reads the title, and reinforces their own biases. That's the main problem I was trying to address. 1 - https://slashdot.org/story/25/08/11/2253229/llms-simulated-reasoning-abilities-are-a-brittle-mirage-researchers-find https://slashdot.org/story/25/08/11/2253229/llms-simulated-r...
- willvarfar 1y agoDoing analysis on small models or small data is perfectly valid if the results extrapolate to large models. Which is why right now we're looking at new research papers that are still listing the same small datasets and comparing to the same small models that papers five years ago did.
- NitpickLawyer 1y agoI have nothing against researching this, I think it's important. My main issue is with articles choosing to grab a "conclusion" and imply it extrapolates to larger models, without any support for that. They are going for the catchy title first, fine-print be damned.
- willvarfar 1y agoI was just at the KDD conference and the general consensus agreed with this paper. There was only one keynoter who just made the assumption that LLMs are associated with reasoning, which was jarring as the previous keynoter had just explained at length why we need a neuro-symbolic approach instead. The thing is, I think the current companies making LLMs are _not_ trying to be correct or right. They are just trying to hide it better. In the business future for AI the coding stuff that we focus on on HN - how AI can help/impact us - is just a sideline. The huge-money business future of LLMs is to end consumers not creators and it is product and opinion placement and their path to that is to friendship. They want their assistant to be your friend, then your best friend, then your only friend, then your lover. If the last 15 years of social media has been about discord and polarisation to get engagement, the next 15 will be about friendship and love even though that leads to isolation. None of this needs the model to grow strong reasoning skills. That's not where the real money is. And CoT - whilst super great - is just as effective if it's hiding better that its giving you the wrong answer (by being more internally consistent) than if its giving you a better answer?
- calf 1y agoAs to general consensus, Hinton gave a recent talk, and he seemed adamant that neural networks (which LLMs are) really are doing reasoning. He gives his reasons for it. Is Hinton considered an outlier or?
- kazinator 1y ago> conclusions gathered from toy models and implying this generalises to production LLMs is useless You are just trotting out the tired argument that model size magically fixes the issues, rather than just improves the mirage, and so nothing can be known about models with M parameters by studying models with N < M parameters. Given enough parameters, a miraculous threshold is reached whereby LLMs switch from interpolating to extrapolating. Sure!
- ricardobeat 1y agoThat’s what has been seen in practice though. SOTA LLMs have been shown again and again to solve problems unseen in their data set; and despite their shortcomings they have become extremely useful for a wide variety of tasks.
- loosetypes 1y agoMind linking any examples (or categories) of problems that are definitively not in pre training data but can still be solved by LLMs? Preferably something factual rather than creative, genuinely curious. Dumb question but anything like this that’s written about on the internet will ultimately end up as training fodder, no?
- dcre 1y agoHow about the International Math Olympiad? https://arstechnica.com/ai/2025/07/google-deepmind-earns-gold-in-international-math-olympiad-with-new-gemini-ai/ https://arstechnica.com/ai/2025/07/google-deepmind-earns-gol...
- OtherShrezzing 1y agoI think it is worth writing about simply because it might get the (cost constrained) researcher’s work in front of someone who has the near-unlimited research budgets at one of the big AI companies.
- pxc 1y agoWell now they could use GPT-OSS, but it wasn't out when they began the study. I've recently been taking a look at another paper, from 2023, and subsequent research. It has a morally similar finding, though not focused on "reasoning traces", but it's based on GPT-4: https://proceedings.neurips.cc/paper_files/paper/2023/hash/deb3c28192f979302c157cb653c15e90-Abstract-Conference.html https://proceedings.neurips.cc/paper_files/paper/2023/hash/d...
- kevingadd 1y agoAlmost every mention I've seen of gpt-oss was a complaint that the training on synthetic datasets produced a model that's mostly good at benchmarks. Are benchmarks the great results you're referring to or are there a lot of satisfied users out there that just don't post here on HN? Genuinely curious. I can see how performing well on benchmarks at the expense of everything else counts as great results if that's the point of the model.
- Insanity 1y agoThe results from a smaller model are still viable if the paradigm is identical. Unless you believe that larger volumes of data leads to more (unexplained) emergent properties of the AI. i.e, if you think that a larger volume of training data somehow means the model develops actual reasoning skills, beyond the normal next-token prediction. I do think that larger models will perform better, but not because they fundamentally work differently than the smaller models, and thus the idea behind TFA still stands (in my opinion).
- mirekrusin 1y agoHold on their evaluation tasks are based on rotating letters in text? Isn't this known weak area for token based models?
- Terr_ 1y agoI think that's the point, really: It's a reliable and reproducible weakness, but also one where the model can be trained to elicit impressive-looking "reasoning" about what the problem is and how it "plans" to overcome it. Then when it fails to apply the "reasoning", that's evidence the artificial expertise we humans perceived or inferred is actually some kind of illusion. Kind of like a a Chinese Room scenario: If the other end appears to talk about algebra perfectly well, but just can't do it, that's evidence you might be talking to a language-lookup machine instead of one that can reason.
- hooskerdu 1y agoReminds me of a number of grad students I knew who could “talk circles” around all sorts of subjects but failed to ever be able to apply anything.
- Terr_ 1y agoHeh, but just because a human can fail at something doesn't mean everything that fails at it is human. :p
- famouswaffles 1y agoRight, but if you're saying that something is 'incapable of reasoning' because of a failure mode also found in humans, then either humans are 'incapable of reasoning' or you concede that failure mode isn't a justification for that gross assertion. You can't have it both ways.
- boredhedgehog 1y ago> Then when it fails to apply the "reasoning", that's evidence the artificial expertise we humans perceived or inferred is actually some kind of illusion. That doesn't follow, if the weakness of the model manifests on a different level we wouldn't call rational in a human. For example, a human might have dyslexia, a disorder on the perceptive level. A dyslexic can understand and explain his own limitation, but that doesn't help him overcome it.
- Frieren 1y agoThis assessment fits with my anecdotal evidence. LLMs just cannot reason in any basic way. LLMs have a large knowledge base that can be spit out at a moment notice. But they have zero insight on its contents, even when the information has just been asked a few lines before. Most of the "intelligence" that LLMs show is just the ability to ask in the correct way the correct questions mirrored back to the user. That is why there is so many advice on how to do "proper prompting". That and the fact that most questions have already been asked before as anyone that spend some time in StackOverflow back in the day realized. And memory and not reasoning is what is needed to answer them.
- PeterStuer 1y agoPlease don't tell me you were one of those marking every SO question as duplicate, more often than not missing the entire nuance in the question that made it not a duplicate at all, and the answers to the so called previously asked question utterly unusable? This was one of those infuriating things that drove so many away from SO and jump ship the second there was an alternative.
- ceejayoz 1y agoEvery time I ask people for an example of this, and get one, I agree with the duplicate determination. Sometimes it requires a little skimming of the canonical answers past just the #1 accepted one; sometimes there's a heavily upvoted clarification in a top comment, but it's usually pretty reasonable.
- antihipocrat 1y agoI'm not sure why duplicates were ever considered an issue. For certain subjects (like JS) things evolved so quickly during the height of SO that even a year old answer was outdated. That and search engines seemed to promote more recent content.. so an old answer sank under the ocean of blog spam
- ceejayoz 1y agoSO wanted to avoid being a raw Q&A site in favor of something more like a wiki. If a year-old answer on a canonical question is now incorrect, you edit it.
- Martin_Silenus 1y agoIf only we could train people like that to see their reasoning output...
- deleted 1y ago[deleted]
- syllogism 1y agoIt's interesting that there's still such a market for this sort of take. > In a recent pre-print paper, researchers from the University of Arizona summarize this existing work as "suggest[ing] that LLMs are not principled reasoners but rather sophisticated simulators of reasoning-like text." What does this even mean? Let's veto the word "reasoning" here and reflect. The LLM produces a series of outputs. Each output changes the likelihood of the next output. So it's transitioning in a very large state space. Assume there exists some states that the activations could be in that would cause the correct output to be generated. Assume also that there is some possible path of text connecting the original input to such a success state. The reinforcement learning objective reinforces pathways that were successful during training. If there's some intermediate calculation to do or 'inference' that could be drawn, writing out a new text that makes that explicit might be a useful step. The reinforcement learning objective is supposed to encourage the model to learn such patterns. So what does "sophisticated simulators of reasoning-like text" even mean here? The mechanism that the model uses to transition towards the answer is to generate intermediate text. What's the complaint here? It makes the same sort of sense to talk about the model "reasoning" as it does to talk about AlphaZero "valuing material" or "fighting for the center". These are shorthands for describing patterns of behaviour, but of course the model doesn't "value" anything in a strictly human way. The chess engine usually doesn't see a full line to victory, but in the games it's played, paths which transition through states with material advantage are often good -- although it depends on other factors. So of course the chain-of-thought transition process is brittle, and it's brittle in ways that don't match human mistakes. What does it prove that there are counter-examples with irrelevant text interposed that cause the model to produce the wrong output? It shows nothing --- it's a probabilistic process. Of course some different inputs lead to different paths being taken, which may be less successful.
- bubblyworld 1y agoNot sure why everyone is downvoting you as I think you raise a good point - these anthropomorphic words like "reasoning" are useful as shorthands for describing patterns of behaviour, and are generally not meant to be direct comparisons to human cognition. But it goes both ways. You can still criticise the model on the grounds that what we call "reasoning" in the context of LLMs doesn't match the patterns we associate with human "reasoning" very well (such as ability to generalise to novel situations), which is what I think the authors are doing.
- thisisauserid 1y ago(in mice)
- jongjong 1y agoI've used LLMs to generate code for a custom serverless framework which I wrote from scratch that it had never seen before. The framework follows some industry conventions but applied in a distinct way with some distinct features which I have not yet encountered in any other framework... I'm willing to accept that maybe LLMs cannot invent entirely new concepts but I know for a fact that they can synthesize and merge different unfamiliar concepts in complex logical ways to deliver new capabilities. This is valuable on its own.
- moi2388 1y ago“ the researchers created a carefully controlled LLM environment in an attempt to measure just how well chain-of-thought reasoning works when presented with "out of domain" logical problems that don't match the specific logical patterns found in their training data.” Why? If it’s out of domain we know it’ll fail.
- Octoth0rpe 1y agoI don't think we know that it'll fail, or at least that is not universally accepted as true. Rather, there are claims that given a large enough model / context window, such capabilities emerge. I think skepticism of that claim is warranted. This research validates that skepticism, at least for a certain parameters (model family/size, context size, etc).
- podgorniy 1y ago> Why? If it’s out of domain we know it’ll fail. To see if LLMs adhere to logic or observed "logical" responses are rather reproduction of patterns. I personally enjoy this idea of isolation "logic" from "pattern" and seeing if "logic" will manifest in LLM "thinking" about in "non-patternized" domain. -- Also it's never bad give proves to public that "thinking" (like "intelligence") in AI context isn't the same thing we think about intuitively. -- > If it’s out of domain we know it’ll fail. Below goes question which is out of domain. Yet LLMs handle the replies in what appearing as logical way. ``` Kookers are blight. And shmakers are sin. If peker is blight and sin who is he? ``` It is out of domain and it does not fail (I've put it through thinking gemini 2.5). Now back to article. Is observed logic intristic to LLMs or it's an elaborate form of a pattern? Acoording to article it's a pattern.
- moi2388 1y agoOut of domain means that the type of logic hasn’t been in the training set. “All A are B, All C are D, X is A and B, what is X?” is not outside this domain.
- willvarfar 1y agoIts getting to the nub of whether models can extrapolate instead of interpolate. If they had _succeeded_, we'd all be taking it as proof that LLMs can reason, right?
- afro88 1y agoI have a real world problem I gave o1 when it came out and it got it quite wrong. It's a scheduling problem with 4 different constraints that vary each day, and success criteria that need to be fulfilled over the whole week. GPT-5 Thinking (Think Longer) and Opus 4.1 Extended Thinking both get it right. Maybe this unique problem is somehow a part of synthetic training data? Or maybe it's not and the paper is wrong? Either way, we have models that are much more capable at solving unique problems today.
- sachin_rcz 1y agoModels today also have access to certain tooling or have been reinforced to use that tooling in complicated situations. i.e. Questions of counting letters in word are being answered by using python code in background.
- zerof1l 1y ago> ... that these "reasoning" models can often produce incoherent, logically unsound answers when questions include irrelevant clauses or deviate even slightly from common templates found in their training data. I have encountered this problem numerous times, now. It really makes me believe that the models do not really understand the topic, even the basics but just try to predict the text. One recent example was me asking the model to fix my docker-compose file. In it, there's the `network: host` for the `build` part. The model kept assuming that the container would be running with the host network and kept asking me to remove it as a way to fix my issue, even though it wouldn't do anything for the container that is running. Because container runs on `custom_net` network only. The model was obsessed with it and kept telling me to remove it until I explicitly told that it is not, and cannot be the issue. ``` services: app: build: network: host networks: custom_net: ... ```
- imp0cat 1y agohttps://www.experimental-history.com/p/bag-of-words-have-mercy-on-us https://www.experimental-history.com/p/bag-of-words-have-mer... Here is an explanation.
- burnte 1y ago> It really makes me believe that the models do not really understand the topic, even the basics but just try to predict the text. This is correct. There is no understanding, there aren't even concepts. It's just math, it's what we've been doing with words in computers for decades, just faster and faster. They're super useful in some areas, but they're not smart, they don't think.
- pama 1y agoThe math in the original paper is questionable. By leaving free the choice of divergence in Eq 3, Eq 4 has no practical value except when said divergence is zero exactly.
- ponow 1y ago> LLMs are [...] sophisticated simulators of reasoning-like text Most humans are unsophisticated simulators of reasoning-like text.
- NoGravitas 1y agoExcept you, right? You're one of the special few who can actually reason, not like /those/ people.
- whoknowsidont 1y agoYou're completely missing the point of OP's comment, and strangely, ironically lending credence to your interpretation of that comment lol (self-inflicted harm). We don't have a good scientific or philosophical handle on what it actually means to "think" (let alone consciousness). Humanity has so far been really bad at even using relative heuristics based on our own experiences to recognize, classify, and reason about entities that "think." So it's really amusing when authors just arbitrarily side-step this whole issue and describe these systems as categorically not being real but imitating the real thing... all the while not realizing such characterizations apply to humanity as well.
- deleted 1y ago[deleted]
- pllbnk 1y agoWe're rapidly reaching trough of disillusionment with LLMs, and other generative transformer models for that matter. I am happy because it will help a lot of misinformed people understand what is and isn't possible (100+% productivity gains are not).
- ricardorivaldo 1y agodon't know, maybe in the tecnical circles, but for users the thrill is still going on, and rising
- megaloblasto 1y ago100% productivity gains on coding tasks are absolutely within the realm of possibility
- lm28469 1y agoAnd how much of productivity loss due to the insane amount of noise being generated ? (filler ridden reports, emails, videos, podcasts, &c.)
- megaloblasto 1y agoI'm talking about 100% net gain in productivity.
- lm28469 1y agoYou're talking about net gains in "coding tasks" productivity, I'm talking in productivity gain across the board. My company deals with an insane amount of customers who use chatgpt to pre-debug their problems before coming to our support. Once they contact our support they regurgitate llm generated BS to our support engineers thinking they're going to speed up the process, the only thing they're doing is generating noise that slows everyone down because chatgpt has absolutely no clue about our product and keeps sending them on wild goose chases. Sometimes they even lie pretending "a colleague" steered them in this or that direction while it's 100% obvious the whole thing was hallucinate and even written by an llm. I can't tell you how frustrating it is to read a 10 min long customer email just to realise it's just an llm hallucinating probable causes for a bug that takes 2 sentences to describe.
- Workaccount2 1y ago[dead]