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Reasoning models reason well, until they don't

arxiv.org

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Re: Reasoning models reason well, until they don't

#11

Post by fsloth »

sothatsit wrote: post #6
iLoveOncall wrote: post #2

> [...] recent studies show that transformers and LLMs fail catastrophically once reasoning problems exceed modest complexity. We revisit these findings through the lens of large reasoning models (LRMs) -- LLMs fine-tuned with incentives for step-by-step argumentation and self-verification This was the obvious outcome of the study (don't get me wrong, obvious outcomes are still worth having research on). "LRMs" *are*…

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 m…

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.

Re: Reasoning models reason well, until they don't

#12

Post by qsort »

iLoveOncall wrote: post #2

> [...] recent studies show that transformers and LLMs fail catastrophically once reasoning problems exceed modest complexity. We revisit these findings through the lens of large reasoning models (LRMs) -- LLMs fine-tuned with incentives for step-by-step argumentation and self-verification This was the obvious outcome of the study (don't get me wrong, obvious outcomes are still worth having research on). "LRMs" *are*…

Don't they have a significant RL component? The "we'll just make it bigger" idea that was peddled a lot after GPT3.5 was nonsense, but that's not the only thing they're doing right now.

Re: Reasoning models reason well, until they don't

#15

Post by sothatsit »

fsloth wrote: post #11
sothatsit wrote: post #6

Earlier quoted context omitted.

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 m…

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 jus…

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.

Re: Reasoning models reason well, until they don't

#16

Post by dspillett »

davidhs wrote: post #13
equinox_nl wrote: post #3

But I also fail catastrophically once a reasoning problem exceeds modest complexity.

Do you? Don't you just halt and say this is too complex?

Some would consider that to be failing catastrophically. The task is certainly failed.

Re: Reasoning models reason well, until they don't

#20

Post by antonvs »

monkeydust wrote: post #8
equinox_nl wrote: post #3

But I also fail catastrophically once a reasoning problem exceeds modest complexity.

But you recognise you are likely to fail and thus dont respond or redirect the problem to someone who has a greater likelihood of not failing.

I’ve had models “redirect the problem to someone who has a greater likelihood of not failing”. Gemini in particular will do this when it runs into trouble.

I don’t find all these claims that models are somehow worse than humans in such areas convincing. Yes, they’re worse in some respects. But when you’re talking about things related to failures and accuracy, they’re mostly superhuman.

For example, how many humans can write hundred of lines of code (in seconds mind you) and regularly not have any syntax errors or bugs?

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