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Understanding Reasoning LLMs

magazine.sebastianraschka.com

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Re: Understanding Reasoning LLMs

#91

Post by visarga »

ggm wrote: post #70
gsam wrote: post #59

Earlier quoted context omitted.

I don't like wading into this debate when semantics are very personal/subjective. But to me, it seems like almost a sleight of hand to add the stochastic part, when actually they're possibly weighted more on the parrot part. Parrots are much more concrete, whereas the term LLM could refer to the general architecture. The question to me seems: If we expand on this architecture (in some direction, compute, size etc.),…

"Expand the architecture" .. "get something much more powerful" .. "more dilithium crystals, captain" Like I said elsewhere in this overall thread, we've been here before. Yes, you do see improvements in larger datasets, weighted models over more inputs. I suggest, I guess I believe (to be more honest) that no amount of "bigger" here will magically produce AGI simply because of the scale effect. There is no theory be…

> this stuff isn't making a qualitative leap into emergent anything.

The magical missing ingredient here is search. AlphaZero used search to surpass humans, and the whole Alpha family from DeepMind is surprisingly strong, but narrowly targeted. The AlphaProof model uses LLMs and LEAN to solve hard math problems. The same problem solving CoT data is being used by current reasoning models and they have much better results. The missing piece was search.

Re: Understanding Reasoning LLMs

#92

Post by colonial »

vector_spaces wrote: post #4

Is there any work being done in training LLMs on more restricted formal languages? Something like a constraint solver or automated theorem prover, but much lower level. Specifically something that isn't natural language. That's the only path I could see towards reasoning models being truly effective I know there is work being done with e.g. Lean integration with ChatGPT, but that's not what I mean exactly -- there's…

If I understand your idea correctly, I don't think a "pure" LLM would derive much advantage from this. Sure, you can constrain them to generate something syntactically valid, but there's no way to make them generate something semantically valid 100% of the time. I've seen frontier models muck up their function calling JSON more than once.

As long as you're using something statistical like transformers, you're going to need deterministic bolt-ons like Lean.

Re: Understanding Reasoning LLMs

#93

Post by rixed »

o11c wrote: post #37
Legend2440 wrote: post #16

Earlier quoted context omitted.

The way the authors talk about LLMs really rubs me the wrong way. They spend more of the paper talking up the 'claims' about LLMs that they are going to debunk than actually doing any interesting study. They came into this with the assumption that LLMs are just a cheap trick. As a result, they deliberately searched for an example of failure, rather than trying to do an honest assessment of generalization capabilities…

What the hype crowd doesn't get is that for most people, "a tool that randomly breaks" is not useful.

The fact that a tool can break or that the company manufacturing that tool lies about its abilities, are annoying but do not imply that the tool is useless.

I experience LLM "reasoning" failure several times a day, yet I find them useful.

Re: Understanding Reasoning LLMs

#95

Post by NitpickLawyer »

daxfohl wrote: post #87

But how on earth do you train it? With regular LLMs, you get feedback on each word / token you generate, as you can match against training text. With these, you've got to generate hundreds of tokens in the thinking block fiest, and even after that, there's no "matching" next word, only a full solution. And it's either right or wrong, no probabilities to do a gradient on.

> only a full solution. And it's either right or wrong, no probabilities to do a gradient on.

You could use reward functions that do a lot more complicated stuff than "ground_truth == boxed_answer". You could, for example split the "CoT" in paragraphs, and count how many paragraphs match whatever you consider a "good answer" in whatever topic you're trying to improve. You can use embeddings, or fuzzy string matches, or even other LLMs / reward models.

I think math and coding were explored first because they're easier to "score", but you could attempt it with other things as well.

Re: Understanding Reasoning LLMs

#96

Post by colordrops »

The article talks about how you should choose the right tool for the job, meaning that reasoning and non reasoning models have tradeoffs, and lists a table of criteria for selecting between model classes. Why couldn't a single model choose to reason or not itself? Or is this what "mixture of experts" is?

Re: Understanding Reasoning LLMs

#97

Post by Davidzheng »

daxfohl wrote: post #87

But how on earth do you train it? With regular LLMs, you get feedback on each word / token you generate, as you can match against training text. With these, you've got to generate hundreds of tokens in the thinking block fiest, and even after that, there's no "matching" next word, only a full solution. And it's either right or wrong, no probabilities to do a gradient on.

right or wrong gives a loss -> gradient

Re: Understanding Reasoning LLMs

#98

Post by raincole »

vector_spaces wrote: post #4

Is there any work being done in training LLMs on more restricted formal languages? Something like a constraint solver or automated theorem prover, but much lower level. Specifically something that isn't natural language. That's the only path I could see towards reasoning models being truly effective I know there is work being done with e.g. Lean integration with ChatGPT, but that's not what I mean exactly -- there's…

AlphaProof. Although I don't know if it's large enough to be called an LLM.

https://deepmind.google/discover/blog/ai-solves-imo-problems...

Re: Understanding Reasoning LLMs

#99

Post by root_axis »

ngneer wrote: post #19

Nice article. >Whether and how an LLM actually "thinks" is a separate discussion. The "whether" is hardly a discussion at all. Or, at least one that was settled long ago. "The question of whether a computer can think is no more interesting than the question of whether a submarine can swim." --Edsger Dijkstra

That doesn't really settle it, just dismiss the question. The submarine analogy could be interpreted to support either conclusion.

Re: Understanding Reasoning LLMs

#100

Post by martin-t »

dang wrote: post #90
martin-t wrote: post #41

Earlier quoted context omitted.

[flagged]

Please don't cross into personal attack and please don't post in the flamewar style, regardless of how wrong someone is or you feel they are. We're trying for the opposite here. https://news.ycombinator.com/newsguidelines.html

The issue with this approach to moderation is that it targets posts based on visibility of "undesired" behavior instead of severity.

For example, many manipulative tactics (e.g. the fake sorry here, responding to something else than was said, ...) and lying can be considered insults (they literally assume the reader is not smart enough to notice, hence at least as severe as calling someone an idiot) but it's hard for a mod to notice without putting in a lot of effort to understand the situation.

Yet when people (very mildly) punish this behavior by calling it out, they are often noticed by the mod because the call out is more visible.

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