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What do you mean by reasoning? If you mean solving logic problems, then reasoning LLMs seem to pass that bar as they do very well programming and maths competi

What do you mean by reasoning? If you mean solving logic problems, then reasoning LLMs seem to pass that bar as they do very well programming and maths competitions. Reasoning LLMs can also complete problems like multiplying large numbers, which requires applying some sort of algorithm where the results cannot just be memorised. They also do this much better than standard pre-trained LLMs with no RL. So, that makes me come back to this question of what definition of reasoning do people use that reasoning models do not meet? They're not perfect, obviously, but that is not a requirement of reasoning if you agree that humans can reason. We make mistakes as well, and we also suffer under higher complexity. Perhaps they are less reliable in knowing when they have made mistakes or not than trained humans, but I wouldn't personally include reliability in my definition for reasoning (just look at how often humans make mistakes in tests). I am yet to see any serious, reasoned, arguments that suggest why the amazing achievements of reasoning LLMs in maths and programming competitions, on novel problems, does not count as "real reasoning". It seems much more that people just don't like the idea of LLMs reasoning, and so reject the idea without giving an actual reason themselves, which seems somewhat ironic to me.

fsloth 1y ago
I guess we mean here ”usefull reasoning” instead of the idiot-savant. I mean it’s a fair ask since these are marketed as _tools_ you can use to implement _industrial processes_ and even replace you human workers. In that I guess the model does not need to be the most reasonable intepreter of vague and poorly formulated user inputs but I think to improve a bit at least, to become usefull general appliances and not just test-scoring-automatons. The key differentiator here is that tests generally _are made to be unambiguously scoreable_. Real world problems are often more vague from the point of view of optimal outcome.
- sothatsit 1y ago
  Thanks. So, people are extending "reasoning" to include making good decisions, rather than just solving logic problems. That makes sense to me that if people use that definition, LLMs are pretty bad at "reasoning". Although, I would argue that this is not reasoning at all, but rather "common sense" or the ability to have a broader perspective or think of the future. These are tasks that come with experience. That is why these do not seem like reasoning tasks to me, but rather soft skills that LLMs lack. In my mind these are pretty separate concerns to whether LLMs can logically step through problems or apply algorithms, which is what I would call reasoning.
  - hansmayer 1y ago
    Ah yes then, let me then unchain my LLM on those nasty unsolved math and logic problems I've absolutely not be struggling with in the course of my career.
    - sothatsit 1y ago
      A lot of maths students would also struggle to contribute to frontier math problems, but we would still say they are reasoning. Their skill at reasoning might not be as good as professional mathematicians, but that does not stop us from recognising that they can solve logic problems without memorisation, which is a form of reasoning. I am just saying that LLMs have demonstrated they can reason, at least a little bit. Whereas it seems other people are saying that LLM reasoning is flawed, which does not negate the fact that they can reason, at least some of the time. Maybe generalisation is one area where LLM's reasoning is weakest though. They can be near-elite performance at nicely boxed up competition math problems, but their performance dramatically drops on real-world problems where things aren't so neat. We see similar problems in programming as well. I'd argue the progress on this has been promising, but other people would probably vehemently disagree with that. Time will tell.
      - vidarh 1y ago
        Thank you for picking at this. A lot of people appear to be - often not consciously or intentionally - setting the bar for "reasoning" at a level many or most people would not meet. Sometimes that is just a reaction to wanting an LLM that is producing result that is good for their own level. Sometimes it reveals a view of fellow humans that would be quite elitist if stated outright. Sometimes it's a kneejerk attempt at setting the bar at a point that would justify a claim that LLMs aren't reasoning. Whatever the reason, it's a massive pet peeve of mine that it is rarely made explicit in these conversations, and it makes a lot of these conversations pointless because people keep talking past each other. For my part a lot of these models often clearly reason by my standard, even if poorly. People also often reason poorly, even when they demonstrably attempt to reason step by step. Either because they have motivations to skip over uncomfortable steps, or because they don't know how to do it right. But we still would rarely claim they are not capable of reasoning. I wish more evaluations of LLMs would establish a human baseline to test them against for much this reason. It would be illuminating in terms of actually telling us more about how LLMs match up to humans in different areas.
- hansmayer 1y ago
  ^^This is a great view and it seems generally widely understood by the file and rank techies. I feel pitty for the general public retail investors which are about to be left holding the bag for the VCs, after a certain major <ahem> champion goes into IPO soon.
js8 1y ago
> So, that makes me come back to this question of what definition of reasoning do people use that reasoning models do not meet? The models can learn reasoning rules, but they are not able to apply them consistently or recognize the rules they have learned are inconsistent. (See also my other comment which references comments I made earlier.) And I think they can't without a tradeoff, as I commented https://news.ycombinator.com/item?id=45717855 https://news.ycombinator.com/item?id=45717855 ; the consistency requires certain level of close-mindedness.
- sothatsit 1y ago
  Yes, so I think in this case we use different definitions of reasoning. You include reliability as a part of reasoning, whereas I do not. I would argue that humans are not 100% reliable in their reasoning, and yet we still claim that they can reason. So, even though I would agree that the reasoning of LLMs is much less reliable, careful, and thoughtful than smart humans, that does not mean that they are not reasoning. Rather, it means that their reasoning is more unreliable and less well-applied than people. But they are still performing reasoning tasks (even if their application of reasoning can be flawed). Maybe the problem is that I am holding out a minimum bar for LLMs to jump to count as reasoning (demonstrated application of logical algorithms to solve novel problems in any domain), whereas other people are holding the bar higher (consistent and logical application of rules in all/most domains).
  - js8 1y ago
    The problem is if you're not able to apply the reasoning rules consistently, then you will always fail on large enough problem. If you have an inconsistent set of reasoning rules, then you can set up a problem as a trap so that the reasoning fails. You can argue that damaged toaster is still a toaster, conceptually. But if it doesn't work, then it's useless. As it stands, models lack ability to reason because they can fail to reason and you can't do anything about it. In case of humans, it's valid to say they can reason, because humans can at least fix themselves, models can't.
    - sothatsit 1y ago
riku_iki 1y ago
> then reasoning LLMs seem to pass that bar as they do very well programming and maths competitions. it could be this is just result of good stochastic parroting and not reasoning. Both of those niches are narrow with high amount of training data (e.g. corps buying solutions from leetcode and training LLMs on them). From another hand we see that LLMs fail in more complex environment: e.g. ask to build some new feature in postgres database.
- sothatsit 1y ago
  This is clearly false. LLMs being able to multiply large numbers is the clear example to me that there is more than just memorisation going on. They cannot just memorise the answers to multipling huge numbers like they do. That's not to mention that these programming competition problems are designed to be novel. They are as novel as the competition designers can get while sticking to the bounds of the competition. This is clearly not stochastic parrot behaviour. Additionally, them falling over in large codebases is not evidence that they cannot reason over smaller well-defined problems. It is just evidence that their reasoning has limits, which should not be surprising to anyone. Humans also have limits in our reasoning. That does not mean we do not reason.
  - riku_iki 1y ago
    I think you just made lots of handwaving statements. Here is result which says LLMs can't do multi-digit multiplications well: https://arxiv.org/pdf/2510.00184 https://arxiv.org/pdf/2510.00184
    - sothatsit 1y ago
      We are talking about reasoning models here, not old non-reasoning models like Llama-90B and GPT-4. Obviously, they cannot multiply numbers. That was never in question. Maybe at least give a cursory glance at a paper before trying to cite it to support your point? I find it fun that this paper also points out that using another training method, IcoT, they can produce models that can multiply numbers perfectly. The frontier reasoning models can still make mistakes, they just get very close a lot of the time, even with 10-20 digit numbers. But the IcoT models can do it perfectly, they just can only multiply numbers.

fsloth 1y ago

I guess we mean here ”usefull reasoning” instead of the idiot-savant. I mean it’s a fair ask since these are marketed as _tools_ you can use to implement _industrial processes_ and even replace you human workers. In that I guess the model does not need to be the most reasonable intepreter of vague and poorly formulated user inputs but I think to improve a bit at least, to become usefull general appliances and not just test-scoring-automatons. The key differentiator here is that tests generally _are made to be unambiguously scoreable_. Real world problems are often more vague from the point of view of optimal outcome.

sothatsit 1y ago
Thanks. So, people are extending "reasoning" to include making good decisions, rather than just solving logic problems. That makes sense to me that if people use that definition, LLMs are pretty bad at "reasoning". Although, I would argue that this is not reasoning at all, but rather "common sense" or the ability to have a broader perspective or think of the future. These are tasks that come with experience. That is why these do not seem like reasoning tasks to me, but rather soft skills that LLMs lack. In my mind these are pretty separate concerns to whether LLMs can logically step through problems or apply algorithms, which is what I would call reasoning.
- hansmayer 1y ago
  Ah yes then, let me then unchain my LLM on those nasty unsolved math and logic problems I've absolutely not be struggling with in the course of my career.
  - sothatsit 1y ago
    A lot of maths students would also struggle to contribute to frontier math problems, but we would still say they are reasoning. Their skill at reasoning might not be as good as professional mathematicians, but that does not stop us from recognising that they can solve logic problems without memorisation, which is a form of reasoning. I am just saying that LLMs have demonstrated they can reason, at least a little bit. Whereas it seems other people are saying that LLM reasoning is flawed, which does not negate the fact that they can reason, at least some of the time. Maybe generalisation is one area where LLM's reasoning is weakest though. They can be near-elite performance at nicely boxed up competition math problems, but their performance dramatically drops on real-world problems where things aren't so neat. We see similar problems in programming as well. I'd argue the progress on this has been promising, but other people would probably vehemently disagree with that. Time will tell.
    - vidarh 1y ago
      Thank you for picking at this. A lot of people appear to be - often not consciously or intentionally - setting the bar for "reasoning" at a level many or most people would not meet. Sometimes that is just a reaction to wanting an LLM that is producing result that is good for their own level. Sometimes it reveals a view of fellow humans that would be quite elitist if stated outright. Sometimes it's a kneejerk attempt at setting the bar at a point that would justify a claim that LLMs aren't reasoning. Whatever the reason, it's a massive pet peeve of mine that it is rarely made explicit in these conversations, and it makes a lot of these conversations pointless because people keep talking past each other. For my part a lot of these models often clearly reason by my standard, even if poorly. People also often reason poorly, even when they demonstrably attempt to reason step by step. Either because they have motivations to skip over uncomfortable steps, or because they don't know how to do it right. But we still would rarely claim they are not capable of reasoning. I wish more evaluations of LLMs would establish a human baseline to test them against for much this reason. It would be illuminating in terms of actually telling us more about how LLMs match up to humans in different areas.
hansmayer 1y ago
^^This is a great view and it seems generally widely understood by the file and rank techies. I feel pitty for the general public retail investors which are about to be left holding the bag for the VCs, after a certain major <ahem> champion goes into IPO soon.

js8 1y ago

> So, that makes me come back to this question of what definition of reasoning do people use that reasoning models do not meet? The models can learn reasoning rules, but they are not able to apply them consistently or recognize the rules they have learned are inconsistent. (See also my other comment which references comments I made earlier.) And I think they can't without a tradeoff, as I commented https://news.ycombinator.com/item?id=45717855 https://news.ycombinator.com/item?id=45717855 ; the consistency requires certain level of close-mindedness.

sothatsit 1y ago
Yes, so I think in this case we use different definitions of reasoning. You include reliability as a part of reasoning, whereas I do not. I would argue that humans are not 100% reliable in their reasoning, and yet we still claim that they can reason. So, even though I would agree that the reasoning of LLMs is much less reliable, careful, and thoughtful than smart humans, that does not mean that they are not reasoning. Rather, it means that their reasoning is more unreliable and less well-applied than people. But they are still performing reasoning tasks (even if their application of reasoning can be flawed). Maybe the problem is that I am holding out a minimum bar for LLMs to jump to count as reasoning (demonstrated application of logical algorithms to solve novel problems in any domain), whereas other people are holding the bar higher (consistent and logical application of rules in all/most domains).
- js8 1y ago
  The problem is if you're not able to apply the reasoning rules consistently, then you will always fail on large enough problem. If you have an inconsistent set of reasoning rules, then you can set up a problem as a trap so that the reasoning fails. You can argue that damaged toaster is still a toaster, conceptually. But if it doesn't work, then it's useless. As it stands, models lack ability to reason because they can fail to reason and you can't do anything about it. In case of humans, it's valid to say they can reason, because humans can at least fix themselves, models can't.
  - sothatsit 1y ago

riku_iki 1y ago

> then reasoning LLMs seem to pass that bar as they do very well programming and maths competitions. it could be this is just result of good stochastic parroting and not reasoning. Both of those niches are narrow with high amount of training data (e.g. corps buying solutions from leetcode and training LLMs on them). From another hand we see that LLMs fail in more complex environment: e.g. ask to build some new feature in postgres database.

sothatsit 1y ago
This is clearly false. LLMs being able to multiply large numbers is the clear example to me that there is more than just memorisation going on. They cannot just memorise the answers to multipling huge numbers like they do. That's not to mention that these programming competition problems are designed to be novel. They are as novel as the competition designers can get while sticking to the bounds of the competition. This is clearly not stochastic parrot behaviour. Additionally, them falling over in large codebases is not evidence that they cannot reason over smaller well-defined problems. It is just evidence that their reasoning has limits, which should not be surprising to anyone. Humans also have limits in our reasoning. That does not mean we do not reason.
- riku_iki 1y ago
  I think you just made lots of handwaving statements. Here is result which says LLMs can't do multi-digit multiplications well: https://arxiv.org/pdf/2510.00184 https://arxiv.org/pdf/2510.00184
  - sothatsit 1y ago
    We are talking about reasoning models here, not old non-reasoning models like Llama-90B and GPT-4. Obviously, they cannot multiply numbers. That was never in question. Maybe at least give a cursory glance at a paper before trying to cite it to support your point? I find it fun that this paper also points out that using another training method, IcoT, they can produce models that can multiply numbers perfectly. The frontier reasoning models can still make mistakes, they just get very close a lot of the time, even with 10-20 digit numbers. But the IcoT models can do it perfectly, they just can only multiply numbers.