Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Understanding Reasoning LLMs

magazine.sebastianraschka.com

Post reply on HN
Print view View original

21–30 of 196 posts

Re: Understanding Reasoning LLMs

#21

Post by suddenlybananas »

Legend2440 wrote: post #17
aithrowawaycomm wrote: post #8

I like Raschka's writing, even if he is considerably more optimistic about this tech than I am. But I think it's inappropriate to claim that models like R1 are "good at deductive or inductive reasoning" when that is demonstrably not true, they are incapable of even the simplest "out-of-distribution" deductive reasoning: https://xcancel.com/JJitsev/status/1883158738661691878 They are certainly capable of doing is a wi…

>But I think it's inappropriate to claim that models like R1 are "good at deductive or inductive reasoning" when that is demonstrably not true, they are incapable of even the simplest "out-of-distribution" deductive reasoning: That's not actually what your link says. The tweet says that it solves the simple problem (that they originally designed to foil base LLMs) so they had to invent harder problems until they foun…

Did you see how similar the more complicated problem is? It's nearly the exact same problem.

Re: Understanding Reasoning LLMs

#22

Post by _yb2s »

aithrowawaycomm wrote: post #8

I like Raschka's writing, even if he is considerably more optimistic about this tech than I am. But I think it's inappropriate to claim that models like R1 are "good at deductive or inductive reasoning" when that is demonstrably not true, they are incapable of even the simplest "out-of-distribution" deductive reasoning: https://xcancel.com/JJitsev/status/1883158738661691878 They are certainly capable of doing is a wi…

> they are incapable of even the simplest "out-of-distribution" deductive reasoning

But the link demonstrates the opposite- these models absolutely are able to reason out of distribution, just not with perfect fidelity. The fact that they can do better than random is itself really impressive. And o1-preview does impressively well, only vary rarely getting the wrong answer on variants of that Alice in Wonderland problem.

If you would listen to most of the people critical of LLMs saying they're a "stochastic parrot" - it should be impossible for them to do better than random on any out of distribution problem. Even just changing one number to create a novel math problem should totally stump them and result in entirely random outputs, but it does not.

Overall, poor reasoning that is better than random but frequently gives the wrong answer is fundamentally, categorically entirely different from being incapable of reasoning.

Re: Understanding Reasoning LLMs

#23

Post by suddenlybananas »

Legend2440 wrote: post #16
scarmig wrote: post #12

Earlier quoted context omitted.

> But I think it's inappropriate to claim that models like R1 are "good at deductive or inductive reasoning" when that is demonstrably not true, they are incapable of even the simplest "out-of-distribution" deductive reasoning: https://xcancel.com/JJitsev/status/1883158738661691878 Your link says that R1, not all models like R1, fails at generalization. Of particular note: > We expose DeepSeek R1 to the variations of…

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…

>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.

And lo and behold, they still found a glaring failure. You can't fault them for not buying into the hype.

Re: Understanding Reasoning LLMs

#24

Post by Legend2440 »

suddenlybananas wrote: post #23
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…

>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. And lo and behold, they still found a glaring failure. You can't fault them for not buying into the hype.

But it is still dishonest to declare reasoning LLMs a scam simply because you searched for a failure mode.

If given a few hundred tries, I bet I could find an example where you reason poorly too. Wikipedia has a whole list of common failure modes of human reasoning: https://en.wikipedia.org/wiki/List_of_fallacies

Re: Understanding Reasoning LLMs

#25

Post by cwillu »

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

The document that quote comes from is hardly a definitive discussion of the topic.

“[…] it tends to divert the research effort into directions in which science can not—and hence should not try to—contribute.” is a pretty myopic take.

--http://www.cs.utexas.edu/users/EWD/ewd08xx/EWD898.PDF

Re: Understanding Reasoning LLMs

#26

Post by janalsncm »

Nice explainer. The R1 paper is a relatively easy read. Very approachable, almost conversational.

I say this because I am constantly annoyed by poor, opaque writing in other instances. In this case, DS doesn’t need to try to sound smart. The results speak for themselves.

I recommend anyone who is interested in the topic to read the R1 paper, their V3 paper, and DeepSeekMath paper. They’re all worth it.

Re: Understanding Reasoning LLMs

#27

Post by danielmarkbruce »

_yb2s wrote: post #22
aithrowawaycomm wrote: post #8

I like Raschka's writing, even if he is considerably more optimistic about this tech than I am. But I think it's inappropriate to claim that models like R1 are "good at deductive or inductive reasoning" when that is demonstrably not true, they are incapable of even the simplest "out-of-distribution" deductive reasoning: https://xcancel.com/JJitsev/status/1883158738661691878 They are certainly capable of doing is a wi…

> they are incapable of even the simplest "out-of-distribution" deductive reasoning But the link demonstrates the opposite- these models absolutely are able to reason out of distribution, just not with perfect fidelity. The fact that they can do better than random is itself really impressive. And o1-preview does impressively well, only vary rarely getting the wrong answer on variants of that Alice in Wonderland probl…

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

Re: Understanding Reasoning LLMs

#29

Post by [deleted] »

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

[deleted]

Re: Understanding Reasoning LLMs

#30

Post by Jensson »

_yb2s wrote: post #22
aithrowawaycomm wrote: post #8

I like Raschka's writing, even if he is considerably more optimistic about this tech than I am. But I think it's inappropriate to claim that models like R1 are "good at deductive or inductive reasoning" when that is demonstrably not true, they are incapable of even the simplest "out-of-distribution" deductive reasoning: https://xcancel.com/JJitsev/status/1883158738661691878 They are certainly capable of doing is a wi…

> they are incapable of even the simplest "out-of-distribution" deductive reasoning But the link demonstrates the opposite- these models absolutely are able to reason out of distribution, just not with perfect fidelity. The fact that they can do better than random is itself really impressive. And o1-preview does impressively well, only vary rarely getting the wrong answer on variants of that Alice in Wonderland probl…

> If you would listen to most of the people critical of LLMs saying they're a "stochastic parrot" - it should be impossible for them to do better than random on any out of distribution problem. Even just changing one number to create a novel math problem should totally stump them and result in entirely random outputs, but it does not.

You don't seem to understand how they work, they recurse their solution meaning if they have remembered components it parrots back sub solutions. Its a bit like a natural language computer, that way you can get them to do math etc, although the instruction set isn't of a turing language.

They can't recurse sub sub parts they haven't seen, but problems that has similar sub parts can of course be solved, anyone understands that.

Post reply on HN