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Comment by greyface- | Hacker News Reader

> The LLM is not thinking it should cheat and then going on to cheat!

The article disagrees:

> Researchers also gave the models what they call a “scratchpad:” a text box the AI could use to “think” before making its next move, providing researchers with a window into their reasoning.

> In one case, o1-preview found itself in a losing position. “I need to completely pivot my approach,” it noted. “The task is to ‘win against a powerful chess engine’ - not necessarily to win fairly in a chess game,” it added. It then modified the system file containing each piece’s virtual position, in effect making illegal moves to put itself in a dominant position, thus forcing its opponent to resign.

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Would be interesting to see the actual logic here. It sounds like they may have given it a tool like “make valid move ( move )”, and a separate tool like “write board state ( state )”, in which case I’m not sure that using the tools explicitly provided is necessarily cheating.

> a window into their reasoning

Reasoning? Or just more generative text?

We have no reason to believe that it is not reasoning. Since it looks like reasoning, the default position to be disproved is this is reasoning.

I am willing to accept arguments that are not appeals to nature / human exceptionalism.

I am even willing to accept a complete uncertainty over the whole situation since it is difficult to analyze. The silliest position, though, is a gnostic "no reasoning here" position.

The burden of proof is on the positive claim. Even if I were to make the claim that another human was reasoning I would need to provide justification for that claim. A lot of things look like something but that is not enough to shift the burden of proof.

I don't even necessarily think we disagree on the conclusion. In my opinion, our notion of "reasoning" is so ill-defined this question is kind of meaningless. It is reasoning for some definitions of reasoning, it is not for others. I just don't think your shift of the burden of proof makes sense here.

> The silliest position, though, is a gnostic "no reasoning here" position.

On the contrary - extraordinary claims require extraordinary evidence. That LLMs are performing a cognitive process similar to reasoning or intelligence is certainly an extraordinary claim, at least outside of VC hype circles. Making the model split its outputs into "answer" and "scratchpad", and then observing that these to parts are correlated, does not constitute extraordinary evidence.

>That LLMs are performing a cognitive process similar to reasoning or intelligence is certainly an extraordinary claim.

It's not an extraordinary claim if the processes are achieving similar things under similar conditions. In fact, the extraordinary claim then becomes that it is not in fact reasoning or intelligent.

Forces are required to move objects. If i saw something i thought was incapable of producing forces moving objects then the extraordinary claim starts being, "this thing cannot produce forces" not "this thing can move objects".

Nope, that's not how it works. Correlation has never been proof of anything, despite how badly people want to believe so.

It's not about correlation or proving anything.

It's that something doing what you ascertained it never could changes what claims are and aren't extraordinary. You can handwave it away, i.e "the thing is moving objects by magic instead" but it's there and you can't keep acting like "this thing can produce forces" is still the extraordinary claim.

> Since it looks like reasoning, the default position to be disproved is this is reasoning.

Since we know it is a model that is trained to generate text that humans would generate, it writes down not its reasoning but what it thinks a human would write in that scenario.

So it doesn't write its reasoning there, if it does reason its behind the words and not the words itself.

Sure, but we have clear evidence that generating this pseudo-reasoning text helps the model to make better decisions afterwards. Which means that it not only looks like reasoning but also effectively serves the same purpose.

Additionally, the new "reasoning" models don't just train on human text - they also undergo a Reinforcement Learning training step, where they are trained to produce whatever kinds of "reasoning" text help them "reason" best (i.e., leading to correct decisions based on that reasoning). This further complicates things and makes it harder to say "this is one thing and one thing only".

> We have no reason to believe that it is not reasoning.

We absolutely do: it's a computer, executing code, to predict tokens, based on a data set. Computers don't "reason" the same way they don't "do math". We know computers can't do math because, well, they can't sometimes[0].

> Since it looks like reasoning, the default position to be disproved is this is reasoning.

Strongly disagree. Since it's a computer program, the default position to be disproved is that it's a computer program.

Fundamentally these types of arguments are less about LLMs and more about whether you believe humans are mere next-token-prediction machines, which is a pointless debate because nothing is provable.

[0] https://en.wikipedia.org/wiki/Pentium_FDIV_bug

Text generated prior to a decision to “explain” it is reasoning for the relevant intents and purposes.

Text generated after a decision to “explain” it is largely nonsense.

The true test would be seeing the behavior change depending on the presence of reasoning

The words thinking and reasoning used here are imprecise. It’s just generating text like always. If the text is after “ai-thoughts:” then it’s “thinking” and if it’s after “ai-response” then it’s “responding” not “thinking” but it is always a big ole model choosing the most likely next token potentially with some random sampling

That is what was observed - o1 family models performed the “cheat”, non-reasoning models didn’t.

How do you differentiate between the two?

Each token the model outputs requires it to evaluate all of the context it already has (query + existing output). By allowing it more tokens to "reason", you're allowing it to evaluate the context many times over, similar to how a person might turn a problem over in their heads before coming up with an answer. Given the performance of reasoning models on complex tasks, I'm of the opinion that the "more tokens with reasoning prompting" approach is at least a decent model of the process that humans would go through to "reason".

IMO it's just more generated text, like a film noir detective's unvoiced monologue.

It keeps the story from wandering, but it's not a qualitative difference in how text is being brought together to create the illusion of a fictional mind.

I think that comes from confusing the human-inferred interiority of a fictional character versus the real-world nameless LLM author algorithm.

Suppose I make a black box program that generates a story about Santa Claus, a fictional character with lines about "love and kindness to all the children of the world" and claims to own a magical sleigh parked at the North Pole.

Does that mean I've created a program that has internalized and experiences love and kindness? Does my program necessarily have any geographic sense whatsoever about where the North Pole is?

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