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.
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.
> [...] 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...
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?
Back to square one!!
It’s only when you need to apply it to domains outside of code, or a domain where it needs to actually reason, that it becomes an issue.
"And the world is more and more complex, and the administrations are less and less prepared"
(~~ Henry Kissinger)
Do you have a link to the video for that talk ?
I think it was Dan Roth who talked about the challenges of reasoning from just adding more layers and it was Chris Manning who just quickly mentioned at the beginning of his talk that LLMs were well known for reasoning.
B) He is also famous for his Doomerism, which often depends on machines doing "reasoning".
So...it's complicated, and we all suffer from confirmation bias.
Ultimately I somehwat disagreed with some of Hintons points in this talk, and after some thought I came up with specific reasons/doubts, and yet at the same time, his intuitive explanations helped shift my views somewhat as well.
I recently had fun asking Gemini to compare how Wittgenstein and Chomsky would view calling a large transformer that was trained entirely on a synthetic 'language' (in my case symbols that encode user behaviour in an app) a 'language' or not. And then, for the killer blow, whether an LLM that is trained on Perl is a language model.
My point being that whilst Hinton is a great and all, I don't think I can quite pin down his definitions of the precise words like reasoning etc. Its possible for people to have opposite meanings for the same words (Wittgenstein famously had two contradictory approaches in his lifetime). In the case of Hinton, I can't quite pin down how loosely or precisely he is using the terms.
A forward-only transformer like GPT can only do symbolic arithmetic to the depth of its layers, for example. And I don't think the solution is to add more layers.
Of course humans are entirely neuro and we somehow manage to 'reason'. So YMMV.
I never thought about it like that, but it sounds plausible.
However, I feel like getting to this stage is even harder to get right compared to reasoning?
Aside from the <0.1% of severely mentally unwell people which already imagine themselves to be in relationships with AIs, I don't think a lot of normal people will form lingering attachments to them without solving the issue of permanence and memory
They're currently essentially stateless, while that's surely enough for short term attachment, I'm not seeing this becoming a bigger issue because if that glaring shortfall.
It'd be like being in a relationship with a person with dementia, thats not a happy state of being.
Honestly, I think this trend is severely overstated until LLMs can sufficiently emulate memories and shared experiences. And that's still fundamentally impossible, just like "real" reasoning with understanding.
So I disagree after thinking about it more - emulated reasoning will likely have a bigger revenue stream via B2E applications compared to emotional attachment in B2C...
Generally, there is a push towards 'context engineering' and there is a lot of bleeding edge research in snapshotting large contexts in ways to get the next back-forth turn in the conversation to be fast etc. So optimisations are already being made.)
What you're saying is like, you can't extrapolate that long division works on 100 digit numbers because you only worked through it using 7 digit numbers and a few small polynomials.
Sometimes, we go so far as to say there is "emergence" of qualitative differences. But really, this is not necessary (and not proven to actually occur).
What is true is that the performance of LLMs at OOD tasks changes with scale.
So no, it's not the same as solving a math problem.
If scaling alone guaranteed strong OOD generalization, we’d expect the largest models to consistently top OOD benchmarks but this isn’t the case. In practice, scaling primarily increases a model’s capacity to represent and exploit statistical relationships present in the training distribution. This reliably boosts in-distribution performance but yields limited gains on tasks that are distributionally distant from the training data, especially if the underlying dataset is unchanged. That’s why trillion parameter models trained on the same corpus may excel at tasks similar to those seen in training, but won’t necessarily show proportional improvements on genuinely novel OOD tasks.
Of course performance improves on the same tasks.
The researchers behind the submitted work chose a certain size and certain size problems, controlling everything. There is no reason to believe that their results won't generalize to larger or smaller models.
Of course, not for the input problems being held constant! That is as strawman.
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.
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!
Dumb question but anything like this that’s written about on the internet will ultimately end up as training fodder, no?
https://arstechnica.com/ai/2025/07/google-deepmind-earns-gol...
In the case of the Math Olympiad, the students who take it grind hours a day for months on practice problems and past Olympiad problems.
Every result is explainable by has having come from training data. That's the null hypothesis.
The alternative hypothesis is that it's not explainable as having come from training data. That's a hard-to-believe, hard-to-prove negative.
You don't get anything out of any computational process that you didn't put in.
Similarly, LLMs do not invent a new way of reasoning about problems or language. They do, however, apply these to unseen problems.
LLMs are one level of abstraction up, but it's a very interesting level of abstraction.
Are you implying models that classify hand-written digits don’t generalize and only work on training data?
I'm saying that this is a strawman version of "not in the training data". The newly handwritten digit is squarely the same sort of stuff that is in the training data: an interpolation.
We are not surprised when we fit a curve to a bunch of points and then find points on the curve that are not exactly any of those points, but are located among the points.
Go too far outside of the cluster of points though and the curve is a hallucination.
This is the intuition behind interpolate vs extrapolate.
We have no idea what the training data is though, so you can't say that.
> and despite their shortcomings they have become extremely useful for a wide variety of tasks.
That seems like a separate question.
O3 pro (but not O3) was successfully able to apply reasoning and math to this domain in interesting ways, much like an expert researcher in these areas would.
Again, the field and the problem is with 100% certainty OOD of the data.
However, the techniques and reasoning methods are of course learned from data. But that's the point, right?
I don't even know that this is possible without seeing the training data. Hence the difficulty in describing how good at "reasoning" O3 Pro is.
The most novel problem would presumably be something only a martian could understand, written in an alien language, the least novel problem would be a basic question taught in preschool like what color is the sky.
Your research falls somewhere between those extremes.
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...
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.
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).
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.
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.
It's a bit like asking human to read text and guess gender or emotional state of the author who wrote it. You just don't have this information.
Similarly you could ask why ":) is smiling and :D is happy" where the question will be seen as "[50372, 382, 62529, 326, 712, 35, 382, 7150]" - encoding looses this information, it's only visible in image rendering of this text.
The point is that if the model were really "reasoning", it would fail differently. Instead, what happens is consistent with it BSing on a textual level.
Suppose a real person outlines a viable plan to work-around their dyslexia, and we watch them not do any of it during the test, and they turn in wrong results while describing the workaround they (didn't) follow. This keeps happening over and over.
In that case, we'd probably conclude they have another problem that isn't dyslexia, such as "parroting something they read somewhere and don't really understand."
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 was one of those infuriating things that drove so many away from SO and jump ship the second there was an alternative.
That and search engines seemed to promote more recent content.. so an old answer sank under the ocean of blog spam
If a year-old answer on a canonical question is now incorrect, you edit it.
SO's biggest asset was its community and while they treated it with some respect in the beginning they took it for granted and trashed it later.
I do agree they later trashed that relationship with the Monica incident and AI policies.
It worked beautifully for quite a while. I don't think anyone anticipated ChatGPT when planning it all out.
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).
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.
> 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.
It is being marketed as directly related to human reasoning.
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.
And Gemini has a note at the bottom about mistakes, and many people discuss this. Caveat emptor, as usual.
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.
Vs
Total AI capex in the past 6 months was greater than US consumer spending
Or
AGI is coming
Or
AI Agents will be able to do most white collar work
——
The paper is addressing parts of the conversation and expectations of AI that are in the HYPE quadrant. There’s money riding on the idea that AI is going to begin to reason reliably. That it will work as a ghost in the machine.
What we have seen the last few years is a conscious marketing effort to rebrand everything ML as AI and to use terms like "Reasoning", "Extended Thinking" and others that for many non technical people give the impression that it is doing far more than it is actually doing.
Many of us here can see his research and be like... well yeah we already knew this. But there is a very well funded effort to oversell what these systems can actually do and that is reaching the people that ultimately make the decisions at companies.
So the question is no longer will AI Agents be able to do most white collar work. They can probably fake it well enough to accomplish a few tasks and management will see that. But will the output actually be valuable long term vs short term gains.
Most people here are going to use a coding agent, be happy about it (like you), and go on their merry way.
Most people here are not making near trillion dollar bets on the world changing power of AI.
EVERYONE here will be affected by those bets. It’s one thing if those bets pay off if future subscription growth matches targets. It’s an entirely different thing if those bets require “reasoning” to pan out.
I like this approach of setting a minimum constraint. But i feel adding more will just make people ignore the point entirely.
LLMs are cool and some of the things they can do now are useful, even surprising. But when it comes to AI, business leaders are talking their books and many people are swept up by that breathless talk and their own misleading intuitions, frequently parroted by the media.
The "but human reasoning is also flawed, so I can't possibly understand what you mean!" objection cannot be sustained in good faith short of delusion.
My dude, have you ever interacted with human reasoning?
Most of what humans think of as reason is actually "will to power". The capability to use our faculties in a way that produces logical conclusions seems like an evolutionary accident, an off-lable use of the brain's machinery for complex social interaction. Most people never learn to catch themselves doing the former when they intended to engage in the latter, some don't know the difference. Fortunately, the latter provides a means of self-correction, the research here hopes to elucidate whether an LLM based reasoning system has the same property.
In other words, given consistent application of reason I would expect a human to eventually draw logically correct conclusions, decline to answer, rephrase the question, etc. But with an LLM, should I expect a non-determisitic infinite walk though plausible nonsense? I expect reaaoning to converge.
What do you think the explanation might be for there being "such a market"?
You cannot even see the comments of people who pointed out the flaws in the study, since they are so heavily downvoted.
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.
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.
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.
“All A are B, All C are D, X is A and B, what is X?” is not outside this domain.
If they had _succeeded_, we'd all be taking it as proof that LLMs can reason, right?
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.
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.
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:
...
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.
Most humans are unsophisticated simulators of reasoning-like text.
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.
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.
I do think that these kinks will eventually work themselves out and actually increase productivity in these areas. People also need to learn that it is not acceptable to just generate some BS and send it to your boss or colleague. That just transfers the real work of understanding the generated content to someone else.
Gusarich•5mo ago
acosmism•5mo ago