> 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.
Doing 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.
I 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.
The extrapolation doesn't work if the transformer is too shallow (too few layers) relative to sequence length, because of https://arxiv.org/abs/2503.03961 . A bunch of tasks become unfeasible when the layer count is too low, and 4 layers is way too low. I.e. linearly increasing the number of layers in a model can result in a superlinear increase in performance on tasks like reasoning.
Aren't most major LLMs moving to an architecture where the model is made up of tons of smaller models?
There's a mountain of reasons why this makes sense from a cost perspective, and seemingly it does also for quality, too, as the newer models train substantially more cheaply and still outperform the older models.
> 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.
That’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.
I 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.
The 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).
>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.
Perhaps 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.
"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.
Almost 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.
Well 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:
It'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.
> The mechanism that the model uses to transition towards the answer is to generate intermediate text.
Yes, which makes sense, because if there's a landscape of states that the model is traversing, and there are probablistically likely pathways between an initial state and the desired output, but there isn't a direct pathway, then training the the model to generate intermediate text in order to move across that landscape so it can reach the desired output state is a good idea.
Presumably LLM companies are aware that there is (in general) no relationship between the generated intermediate text and the output, and the point of the article is that by calling it a "chain of thought" rather than "essentially-meaningless intermediate text which increases the number of potential states the model can reach" users are misled into thinking that the model is reasoning, and may then make unwarranted assumptions, such as that the model could in general apply the same reasoning to similar problems, which is in general not true.
So, you agree with the point that they’re making and you’re mad about it? It’s important to state that the models aren’t doing real reasoning because they are being marketed and sold as if they are.
As for your question: ‘So what does "sophisticated simulators of reasoning-like text" even mean here?’
It means CoT interstitial “reasoning” steps produce text that looks like reasoning, but is just a rough approximation, given that the reasoning often doesn’t line up with the conclusion, or the priors, or reality.
It's not clear what LLMs are good at, and there's great interest in finding out. This is made harder by the frenetic pace of development (GPT 2 came out in 2019). Not surprising at all that there's research into how LLMs fail and why.
Even for someone who kinda understands how the models are trained, it's surprising to me that they struggle when the symbols change. One thing computers are traditionally very good at is symbolic logic. Graph bijection. Stuff like that. So it's worrisome when they fail at it. Even in this research model which is much, much smaller than current or even older models.
Not 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.
If you read the comments of AI articles on Arstechnica, you will find that they seem to have becomes the tech bastion of anti-ai. I'm not sure how it happened, but it seems they found or fell into a strong anti-AI niche, and now feed it.
You cannot even see the comments of people who pointed out the flaws in the study, since they are so heavily downvoted.
> ... 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.
> 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.
I’ve never seen so much misinformation trotted out by the laity as I have with LLMs. It’s like I’m in a 19th century forum with people earnestly arguing that cameras can steal your soul. These people haven’t a clue of the mechanism.
This 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.
> This assessment fits with my anecdotal evidence. LLMs just cannot reason in any basic way.
LLM reasoning is brittle and not like human cognition, but it is far from zero. It has demonstrably improved to a point where it can solve complex, multi-step problems across domains. See the numerous successful benchmarks and out of sample evals (livebench.ai, imo 2025, trackingai.ai IQ, matharena.ai etc).
I gained multiple months of productivity from vibe coding personally in 2025. If being able to correctly code a complex piece of software from a vague, single paragraph description isn't reasoning, what else is? Btw, I don't code UIs. I code complex mathematical algorithms, some of which never found in textbooks.
> 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.
LLMs have excellent recall of recent information within their context window. While they lack human-like consciousness or "insight," their ability to synthesize and re-contextualize information from their vast knowledge base is a powerful capability that goes beyond simple data retrieval.
If anything LLMs show polymath-level ability to synthesize information across domains. How do I know? I use them everyday and get great mileage. It's very obvious.
> 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".
Prompting is the user interface for steering the model's intelligence. However, the model's ability to generate complex, novel, and functional outputs that far exceed the complexity of the input prompt shows that its "intelligence" is more than just a reflection of the user's query.
To summarize, I'm appalled by your statements, as a heavy user of SoTA LLMs on a daily basis for practically anything. I suspect you don't use them enough, and lack a viceral feel or scope for their capabilities.
Please 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.
I'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
I was "playing" the gamification part of StackOverflow. I wanted to ask a good question for points. But it was very difficult because any meaningful question had already been asked. It was way easier to find questions to answer.
Every 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.
>This assessment fits with my anecdotal evidence. LLMs just cannot reason in any basic way.
Agreed completely, and the sentiment seems to be spreading at an ever-increasing rate. I wonder how long it will be before the bubble collapses. I was thinking maybe as long as a few years, but it might be far sooner at this rate. All it will take is one of the large AI companies coming out and publicly stating that they're no longer making meaningful gains or some other way that shows the public what's really going on behind the curtain.
I'm certain the AI hype bubble will be studied for generations as the greatest mass delusion in history (so far).
I'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.
I 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.
> 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.
I 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.
Models 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.
“ 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.”
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.
I 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).
There's a question which was rhetorically asked by Yaser S. Abu-Mostafa: "How do we know if we're learning from data?" and his answer was: "We are learning from data if we can generalize from our training set to our problem set."
To me, it feels a lot like Deming's "what gets measured gets done" (with the quiet part "...oftentimes at the expense of everything else."). Of course, the quiet part is different in this case.
What is this "domain" of which you speak? Because LLMs are supposedly good for flying airplanes, mental health, snakebites, and mushroom poisoning.
We'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).
It is possible to say the same about the low code solutions, e.g. a perfect UI can be used instead of writing a single line of code. The problem is that creating such a system is too resource intensive and counterproductive, and such a system does not exist. Similarly coding has always some problem that cannot be generalised due to the non existent pattern in training, and creating such a pattern beats the goal of having such a system.
The 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.
You'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.
> 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.
Doing 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.
I 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.
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The extrapolation doesn't work if the transformer is too shallow (too few layers) relative to sequence length, because of https://arxiv.org/abs/2503.03961 . A bunch of tasks become unfeasible when the layer count is too low, and 4 layers is way too low. I.e. linearly increasing the number of layers in a model can result in a superlinear increase in performance on tasks like reasoning.
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Aren't most major LLMs moving to an architecture where the model is made up of tons of smaller models?
There's a mountain of reasons why this makes sense from a cost perspective, and seemingly it does also for quality, too, as the newer models train substantially more cheaply and still outperform the older models.
Naively, this seems like it would be relevant.
> 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!
That’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.
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I 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.
The 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).
>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.
Perhaps 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-r...
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"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.
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Almost 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.
Well 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/d...
It'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.
> The mechanism that the model uses to transition towards the answer is to generate intermediate text.
Yes, which makes sense, because if there's a landscape of states that the model is traversing, and there are probablistically likely pathways between an initial state and the desired output, but there isn't a direct pathway, then training the the model to generate intermediate text in order to move across that landscape so it can reach the desired output state is a good idea.
Presumably LLM companies are aware that there is (in general) no relationship between the generated intermediate text and the output, and the point of the article is that by calling it a "chain of thought" rather than "essentially-meaningless intermediate text which increases the number of potential states the model can reach" users are misled into thinking that the model is reasoning, and may then make unwarranted assumptions, such as that the model could in general apply the same reasoning to similar problems, which is in general not true.
Meaningless? The participation in a usefully predicting path is meaning. A different meaning.
And Gemini has a note at the bottom about mistakes, and many people discuss this. Caveat emptor, as usual.
So, you agree with the point that they’re making and you’re mad about it? It’s important to state that the models aren’t doing real reasoning because they are being marketed and sold as if they are.
As for your question: ‘So what does "sophisticated simulators of reasoning-like text" even mean here?’
It means CoT interstitial “reasoning” steps produce text that looks like reasoning, but is just a rough approximation, given that the reasoning often doesn’t line up with the conclusion, or the priors, or reality.
What is "real reasoning"? The mechanism that the models use is well described. They do what they do. What is this article's complaint?
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"the reasoning often doesn’t line up with the conclusion, or the priors, or reality."
My dude, have you ever interacted with human reasoning?
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It's not clear what LLMs are good at, and there's great interest in finding out. This is made harder by the frenetic pace of development (GPT 2 came out in 2019). Not surprising at all that there's research into how LLMs fail and why.
Even for someone who kinda understands how the models are trained, it's surprising to me that they struggle when the symbols change. One thing computers are traditionally very good at is symbolic logic. Graph bijection. Stuff like that. So it's worrisome when they fail at it. Even in this research model which is much, much smaller than current or even older models.
> It's interesting that there's still such a market for this sort of take.
What do you think the explanation might be for there being "such a market"?
Not 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.
""Sam Altman says the perfect AI is “a very tiny model with superhuman reasoning".""
It is being marketed as directly related to human reasoning.
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If you read the comments of AI articles on Arstechnica, you will find that they seem to have becomes the tech bastion of anti-ai. I'm not sure how it happened, but it seems they found or fell into a strong anti-AI niche, and now feed it.
You cannot even see the comments of people who pointed out the flaws in the study, since they are so heavily downvoted.
> ... 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:
```
> 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.
I’ve never seen so much misinformation trotted out by the laity as I have with LLMs. It’s like I’m in a 19th century forum with people earnestly arguing that cameras can steal your soul. These people haven’t a clue of the mechanism.
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https://www.experimental-history.com/p/bag-of-words-have-mer... Here is an explanation.
This 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.
> This assessment fits with my anecdotal evidence. LLMs just cannot reason in any basic way.
LLM reasoning is brittle and not like human cognition, but it is far from zero. It has demonstrably improved to a point where it can solve complex, multi-step problems across domains. See the numerous successful benchmarks and out of sample evals (livebench.ai, imo 2025, trackingai.ai IQ, matharena.ai etc).
I gained multiple months of productivity from vibe coding personally in 2025. If being able to correctly code a complex piece of software from a vague, single paragraph description isn't reasoning, what else is? Btw, I don't code UIs. I code complex mathematical algorithms, some of which never found in textbooks.
> 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.
LLMs have excellent recall of recent information within their context window. While they lack human-like consciousness or "insight," their ability to synthesize and re-contextualize information from their vast knowledge base is a powerful capability that goes beyond simple data retrieval.
If anything LLMs show polymath-level ability to synthesize information across domains. How do I know? I use them everyday and get great mileage. It's very obvious.
> 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".
Prompting is the user interface for steering the model's intelligence. However, the model's ability to generate complex, novel, and functional outputs that far exceed the complexity of the input prompt shows that its "intelligence" is more than just a reflection of the user's query.
To summarize, I'm appalled by your statements, as a heavy user of SoTA LLMs on a daily basis for practically anything. I suspect you don't use them enough, and lack a viceral feel or scope for their capabilities.
Please 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.
I'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
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I was "playing" the gamification part of StackOverflow. I wanted to ask a good question for points. But it was very difficult because any meaningful question had already been asked. It was way easier to find questions to answer.
Every 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.
>This assessment fits with my anecdotal evidence. LLMs just cannot reason in any basic way.
Agreed completely, and the sentiment seems to be spreading at an ever-increasing rate. I wonder how long it will be before the bubble collapses. I was thinking maybe as long as a few years, but it might be far sooner at this rate. All it will take is one of the large AI companies coming out and publicly stating that they're no longer making meaningful gains or some other way that shows the public what's really going on behind the curtain.
I'm certain the AI hype bubble will be studied for generations as the greatest mass delusion in history (so far).
I'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.
Hold on their evaluation tasks are based on rotating letters in text? Isn't this known weak area for token based models?
I 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.
Reminds 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.
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> 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.
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I 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.
Models 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.
“ 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.
> 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.
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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.
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> 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.
Out 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.
I 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).
There's a question which was rhetorically asked by Yaser S. Abu-Mostafa: "How do we know if we're learning from data?" and his answer was: "We are learning from data if we can generalize from our training set to our problem set."
To me, it feels a lot like Deming's "what gets measured gets done" (with the quiet part "...oftentimes at the expense of everything else."). Of course, the quiet part is different in this case.
What is this "domain" of which you speak? Because LLMs are supposedly good for flying airplanes, mental health, snakebites, and mushroom poisoning.
Its 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?
The article already seems outdated on the first day. The key points about SFT are irrelevant in the era of RL.
remind me in 2 days
We'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).
100% productivity gains on coding tasks are absolutely within the realm of possibility
It is possible to say the same about the low code solutions, e.g. a perfect UI can be used instead of writing a single line of code. The problem is that creating such a system is too resource intensive and counterproductive, and such a system does not exist. Similarly coding has always some problem that cannot be generalised due to the non existent pattern in training, and creating such a pattern beats the goal of having such a system.
And how much of productivity loss due to the insane amount of noise being generated ? (filler ridden reports, emails, videos, podcasts, &c.)
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If that is the case, I would argue that you were taking money for doing a job that should've been automated or abstracted already.
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don't know, maybe in the tecnical circles, but for users the thrill is still going on, and rising
The 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.
(in mice)
If only we could train people like that to see their reasoning output...
> LLMs are [...] sophisticated simulators of reasoning-like text
Most humans are unsophisticated simulators of reasoning-like text.
Except you, right? You're one of the special few who can actually reason, not like /those/ people.
You'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.
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'Chain-of-thought AI "degrades significantly" when asked to generalize beyond training.' - yeah thanks Captain Obvious.