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Understanding Reasoning LLMs

magazine.sebastianraschka.com

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Re: Understanding Reasoning LLMs

#81

Post by [deleted] »

jakefromstatecs wrote: post #51
_yb2s wrote: post #35

Earlier quoted context omitted.

> You don't seem to understand how they work I don't think anyone understands how they work- these type of explanations aren't very complete or accurate. Such explanations/models allow one to reason out what types of things they should be capable of vs incapable of in principle regardless of scale or algorithm tweaks, and those predictions and arguments never match reality and require constant goal post shifting as t…

> I don't think anyone understands how they work Yes we do, we literally built them. > We understand how we brought them about via setting up an optimization problem in a specific way, that isn't the same at all as knowing how they work. You're mistaking "knowing how they work" with "understanding all of the emergent behaviors of them" If I build a physics simulation, then I know how it works. But that's a separate q…

[deleted]

Re: Understanding Reasoning LLMs

#82

Post by [deleted] »

onlyrealcuzzo wrote: post #54
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

It's interesting if you're asking the computer to think, which we are. It's not interesting if you're asking it to count to a billion.

[deleted]

Re: Understanding Reasoning LLMs

#83

Post by bglazer »

mitthrowaway2 wrote: post #72
sigbottle wrote: post #61

One thing I don't like about the trend in reasoning LLMs is the over-optimization to coding problems / math problems in particular. A lot of things that aren't well-defined require reasoning, and not just in a "SWE is ambiguous" kind of way - for example, thinking about how to present/teach something in a good way, iterating with the learner, thinking about what context they could be missing, etc. I find that all of…

I think this is because they're trained using RL, and math and coding problems offer an easy way to automatically assess an answer's correctness. I'm not sure how you'd score the correctness of other types of reasoning problems without a lot of manual (and highly subjective!) effort. Perhaps using simulations and games?

Games seem like a really under-explored source of data. It’s an area where humans have an intrinsic motivation to interact with others in dialogue, they can be almost arbitrarily open ended, and there tends to be the kind of clean success/failure end states that RL needs. I’m reminded of the high skill Diplomacy bot that Facebook research built but hasn’t really followed up on.

Re: Understanding Reasoning LLMs

#84

Post by [deleted] »

danielmarkbruce wrote: post #68
jamiek88 wrote: post #58

Earlier quoted context omitted.

Well that’s disappointing. It was an extraordinary claim that really interested me. Thought I was about to be learn! Instead, I just met an asshole.

When someone says "i'm in the cult that believes X", don't expect a water tight argument for the existence of X.

[deleted]

Re: Understanding Reasoning LLMs

#85

Post by daxfohl »

ttul wrote: post #79
daxfohl wrote: post #77

I wonder what it would look like in multi modal, if the reasoning part was an image or video or 3D scene instead of text.

Or just embeddings that only make sense to the model. It’s really arbitrary, after all.

That's what I was thinking too, though with an image you could do a convolution layer and, idk, maybe that makes it imagine visually. Or actually, the reasoning is backwards: the convolution layer is what (potentially) makes that part behave like an image. It's all just raw numbers at the IO layers. But the convolution could keep it from overfitting. And if you also want to give it a little binary array as a scratch pad that just goes straight to the RELUs, why not? Seems more like human reasoning. A little language, a little visual, a little binary / unknown.

Re: Understanding Reasoning LLMs

#86

Post by DiogenesKynikos »

martin-t wrote: post #41
blovescoffee wrote: post #18

Earlier quoted context omitted.

The other day I fed a complicated engineering doc for an architectural proposal at work into R1. I incorporated a few great suggestions into my work. Then my work got reviewed very positively by a large team of senior/staff+ engineers (most with experience at FAANG; ie credibly solid engineers). R1 was really useful! Sorry you don’t like it but I think it’s unfair to say it sucks at reasoning.

[flagged]

How do I know you're reasoning, and not just simulating reasoning (imperfectly)?

Re: Understanding Reasoning LLMs

#87

Post by daxfohl »

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.

Re: Understanding Reasoning LLMs

#88

Post by visarga »

ggm wrote: post #46
bloomingkales wrote: post #31

Earlier quoted context omitted.

There is definitely a mini cult of people that want to be very right about how everyone else is very wrong about AI.

Firstly this is meta ad hom. You're ignoring the argument to target the speaker(s) Secondly, you're ignoring the fact that the community of voices with experience in data sciences, computer science and artificial intelligence themselves are split on the qualities or lack of them in current AI. GPT and LLM are very interesting but say little or nothing to me of new theory of mind, or display inductive logic and reason…

> GPT and LLM are very interesting but say little or nothing to me of new theory of mind, or display inductive logic and reasoning, or even meet the bar for a philosophers cave solution to problems.

The simple fact they can generate language so well makes me think... maybe language itself carries more weight than we originally thought. LLMs can get to this point without personal experience and embodiment, it should not have been possible, but here we are.

I think philosophers are lagging science now. The RL paradigm of agent-environment-reward based learning seems to me a better one than what we have in philiosophy now. And if you look at how LLMs model language as high dimensional embedding spaces .. this could solve many intractable philosophical problems, like the infinite homunculus regress problem. Relational representations straddle the midpoint between 1st and 3rd person, offering a possible path over the hard problem "gap".

Re: Understanding Reasoning LLMs

#89

Post by visarga »

ggm wrote: post #49
danielmarkbruce wrote: post #27

Earlier quoted context omitted.

anyone saying an LLM is a stochastic parrot doesn't understand them... they are just parroting what they heard.

A good literary production. I would have been proud of it had I thought of it, but it's a path to observe a strong "whataboutery" element that if we use "stochastic parrot" as shorthand and you dislike the term, now you understand why we dislike the constant use of "infer", "reason" and "hallucinate" Parrots are self aware, complex reasoning brains which can solve problems in geometry, tell lies, and act socially or…

Well parrots can make more parrots, LLMs can't make their own GPUs. So parrots win, but LLMs can interpolate and even extrapolate a little, have you ever heard a parrot do translation, hearing you say something in English and translating it to Spanish? Yes, LLMs are not parrots. Besides their debatable abilities, they work with human in the loop, which means humans push them outside their original distribution. That's not a parroting act, being able to do more than pattern matching and reproduction.

Re: Understanding Reasoning LLMs

#90

Post by dang »

martin-t wrote: post #41
blovescoffee wrote: post #18

Earlier quoted context omitted.

The other day I fed a complicated engineering doc for an architectural proposal at work into R1. I incorporated a few great suggestions into my work. Then my work got reviewed very positively by a large team of senior/staff+ engineers (most with experience at FAANG; ie credibly solid engineers). R1 was really useful! Sorry you don’t like it but I think it’s unfair to say it sucks at reasoning.

[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

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