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post-it 29 days ago

This is an excellent comment from your HN link:

> The core issue there isn't that the LLM isn't building internal models to represent its world, it's that its world is limited to tokens. Anything not represented in tokens, or token relationships, can't be modeled by the LLM, by definition.

I've noticed that LLMs struggle with tic tac toe, even when I get them to draw it out. And it makes sense! Tic tac toe is an extremely visual game, and difficult to reason about in a single dimension.

solid_fuel 29 days ago

> It's pretty clear to me that above a certain size threshold, LLMs are more than a sum of their parts. The sheer amount of training data seems to embed a higher level of reasoning.

You're staring at a mirror.

I have never seen any compelling evidence that LLMs actually reason. They are good at predicting the next token, and that is all they do. This action may be useful, but it is not reasoning.

There cannot be any reasoning embedded in the model. The algorithm is literally "predict the most likely next token". Anything beyond that is just patterns in the predictions fooling us humans into ascribing more to the system than it is actually producing.

wizzwizz4 30 days ago

LLMs are great big finite state machines. Finite state machines can perform mechanical reasoning. A priori, there can be reasoning embedded in the model. I agree that (these) LLMs don't generally reason (even when they're writing words like "I reason that, since X, we have Y, therefore Z"), but that's not because a model inherently cannot do that.

linzhangrun 30 days ago

Quantity change leads to quality change. You can check out this Kurzgesagt video on emergence: https://www.youtube.com/watch?v=16W7c0mb-rE

thepasch 30 days ago

> The algorithm is literally "predict the most likely next token".

That's confusing the training objective with the learned behavior. It's like saying "Stockfish's algorithm is literally 'minimize this number', and therefore, it can't actually play Chess."

Not a valid comparison. Chess algorithms are built around the rules of chess, most notably the turn taking nature of the game (min/max with alpha/beta pruning based on lists of valid moves in any position).

etskinner 30 days ago

Who's to say the "rules of reasoning" aren't just predicting the next thing that an intelligent person (you) would do? Emergent behavior isn't magic, it's just emergent.

This is just a misconception of how LLMs work and also what reasoning is.

“There cannot be any reasoning embedded in the model” a strong statement, what do you mean by reasoning because by any reasonable definition I’m aware of, they clearly are able to exhibit reasoning.

The fact that the pre training objective is next token loss has nothing to do with capabilities or their ability to reason. To be highly successful at next token prediction you NEED to reason. I’m quite confused here.

LLM output produces the illusion of reasoning. The underlying computation, however, is not reasoning.

If you don’t mind actually taking a few more words to be more specific that would be helpful because what you’re saying doesn’t really make sense at all. You don’t need to trust that the reasoning traces are all faithful representation of an internal reasoning trace. Plenty of other ways to probe models (see anthropics work using circuit tracing).

What else is there to say? LLMs can at most regurgitate approximations of human reasoning steps in the limited forms in which they may be expressed in the training data or interpolations thereof. That's the core essence of what they are. There is no proper reasoning to be found.

"at most" is wrong. RL with verifiable rewards takes you beyond quality and skills represented in training data, I'm not aware of meaningful fundamental limits here if you scale compute enough even though right now it's highly sample inefficient.

Since you refuse to actually define what you consider to be reasoning let me at least put one out there: a system exhibits reasoning when an answer depends on nontrivial intermediate computation over the problem. If you find problems with this, fine, but just make an effort to contribute an alternative.

If you increase test time compute you get better performance. If the model was just "interpolating" this wouldn't really work would it? Models can do FrontierMath expert problems (unpublished, expert authored, peer reviewed math problems) that require an insane amount of compositional reasoning. If they were regurgitating training data, that wouldn't really work would it? Chain of thought, while not always faithful to internal computation, improves performance. If the models were just regurgitating information, it wouldn't work that well would it?

"regurgitating training data" is also of course misleading. Yea they can memorize parts of the training data, but they generalize very well.

thepasch 30 days ago

How do you define reasoning? What does a system have to functionally do in order to qualify for it?

Reasoning includes things like proper use of logic. LLMs have been repeatedly shown to fail horribly at this.

They consistently fail at drawing basic logical conclusions because they cannot build a sufficiently abstract model of certain problems that allows them to grasp their true nature. In other words, the whole class of questions of the kind of "how many r's in strawberry" or "do I take the car to the car wash?" would be answered correctly and reliably.

> Reasoning includes things like proper use of logic. LLMs have been repeatedly shown to fail horribly at this.

That models cannot do ALL logic problems does not mean that they cannot properly use logic...they can write Lean-verified theorems. How is that not logic?

> They consistently fail at drawing basic logical conclusions because they cannot build a sufficiently abstract model of certain problems that allows them to grasp their true nature.

What does their "grasp[ing] their true nature" have anything to do with what they can do?

> In other words, the whole class of questions of the kind of "how many r's in strawberry" or "do I take the car to the car wash?" would be answered correctly and reliably.

Again, just because you have interesting failure modes or brittleness does not mean they do not reason.

applicative 30 days ago

LLM output is a kind of dreaming but with the whole of past human text output as dream material. It turns out to be useful if you can direct the hallucination