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PhD Knowledge Not Required: A Reasoning Challenge for Large Language Models

arxiv.org

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Re: PhD Knowledge Not Required: A Reasoning Challenge for Large Language Models

#81

Post by mort96 »

enum wrote: post #23
XCabbage wrote: post #22

Earlier quoted context omitted.

It's not just that it requires recall. It's that it requires no non-trivial thought beyond recall.

I think what you mean is that once you've managed to recall, checking constraints is easy. Remarkably, a few people are much better at this than others. They are able to think fast and execute an explicit mental search over a very small number of plausible candidates. Other people take forever. Seems to be the case for models too.

I think what you said is the same as what your comment said? "Requires no non-trivial thought besides recall" seems remarkably similar to "once you have recalled an item, checking that it fits the constraints is trivial"

Or are you pointing to a nuanced difference between "easy" and "trivial" that I'm not understanding? Or do you think it requires non-trivial thought before the recall step?

Re: PhD Knowledge Not Required: A Reasoning Challenge for Large Language Models

#82

Post by [deleted] »

ANighRaisin wrote: post #49
armcat wrote: post #39

Earlier quoted context omitted.

It feels like lot of the reasoning tokens go to waste on pure brute force approach - plugging in numbers and evaluating and comparing against the answer. "Nope, that didn't work, let's try 4 instead of 6 this time", etc. What if the reward function instead focuses on diversity of procedures within a token budged (10k - 20k tokens). I.e. RL rewards the model in trying different methods or generating different hypothes…

I would say that diversity isn't something that's easy to reenforce, but I do think it will occur as a natural consequence of optimizing for shorter chains of thought according to a wide variety of problems. Of course, the nature of the data may lead it to do brute force, but that can be fixed with clever fine tuning.

[deleted]

Re: PhD Knowledge Not Required: A Reasoning Challenge for Large Language Models

#83

Post by tucnak »

fzzzy wrote: post #72
Workaccount2 wrote: post #38

Earlier quoted context omitted.

The other day I watched Gemini reasoning go in circles for ~30 seconds trying to determine if 9.11 was greater than 9.9. ---- "Realize a direct comparison of tenths is not enough: Wait, this is not right. In 9.9, the tenths digit is 9. In 9.11, the tenths digit is 1. This initial comparison is incorrectly stated. Let's re-examine. Re-examine tenths place comparison (correctly): In 9.9, the tenths digit is 9. In 9.11,…

Training a model to reason that it should write and execute code is the solution here. Synthetic datasets showing lots of math questions followed by reasoning that writes and executes the program for the answer.

Psst, don't give them ideas keep them to yourself be rich!

Re: PhD Knowledge Not Required: A Reasoning Challenge for Large Language Models

#84

Post by enum »

scotty79 wrote: post #70

If you want to have a problem that is fairly easy for humans but hard for LLMs it should have solution that requires iteratively applying same steps few times. Perhaps conditionally. I predict that LLMs even in chain-of-thought should drop the ball after just few iterations.

Sure. Thats not the goal here. These problems are hard for humans and (mostly) hard for LLMs.

Re: PhD Knowledge Not Required: A Reasoning Challenge for Large Language Models

#85

Post by brokensegue »

enum wrote: post #46
brokensegue wrote: post #43

Are these really reasoning challenges? Seems like they are really solved via brute force or guess and check

Heuristic search, not exhaustive search, is an essential ingredient of reasoning. Has been true since chess. Remains true with MCTS, LLMs, etc.

What heuristics are there for some of these?
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