Comment by lelanthran
8 hours ago
> Why should that matter?
Because we want to use this as a replacement for humans, and the average human can learn the rules of chess without needing to see the rules explained hundreds of thousands of times in millions of games.
So, yeah, it matters if a model has millions of examples of something in its training set and still cannot follow the rules.
We're not talking about learning the rules of chess here, but playing a competent game from just being shown the rules. Why is it so hard for people to keep track of the thread of discussion?
> We're not talking about learning the rules of chess here, but playing a competent game from just being shown the rules.
Okay, lets go with that: it's the "shown the rules" bit that we are arguing about.
The argument is that a human may play maybe a dozen games after learning the rules, after which they won't be inadvertently attempting illegal moves. What we are observing with SOTA models is that, even after seeing millions of chess rules, rulebooks, actual games, etc, they still attempt illegal moves.
This does not point to generalisable and adaptable intelligence, such as we see in the average human.
This is not good reasoning. Humans need at least dozens if not hundreds of reinforcement sessions to only make legal moves, and still occasionally fail (consider pins, discovered check, failing to respond to check). LLMs must one-shot a competent game after imbibing a mass of disconnected units of information about chess. Nothing about the two are similar.
See my comment here for more: https://news.ycombinator.com/item?id=49725306
But we are. The models can't even follow the rules: they try illegal moves all the time.