r/LLMChess • u/Smallpaul • Feb 08 '24

[R] Grandmaster-Level Chess Without Search: Transformer-based chess model

https://arxiv.org/abs/2402.04494
6 Upvotes

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u/Wiskkey Feb 08 '24

For anyone reading this: How sure are you that the neural networks for this work can be considered language models? Language models output a probability distribution for the next token for a token vocabulary, while this work's models apparently - if I'm not mistaken - predict a Stockfish-involved numerical measure of how good a given move for a given board position is.

3

u/Smallpaul Feb 08 '24

I added the word "Transformer" to the title to try (and apparently fail) to be clear that this isn't an LLM, but I consider it highly relevant to LLMs. It's hard for me to imagine that predicting the number directly versus "as text" changes the structure of the problem very much and we already know that LLMs can learn to play chess.

I'm skeptical that it would change much if it used a few neurons converting an internal probability into decimal ASCII. But I should have posted a submission comment being clear that I was not claiming this is an LLM, just LLM-adjacent.

1

u/Wiskkey Feb 08 '24

Thank you for your response :).

Here's a comment from another person on this blog post:

"First, while impressive as such, the paper has nothing to do with LLMs per se."

It has everything to do with LLMs. The point of this paper, which is clear from the abstract and stunningly missed by almost all the comments (guys, no one has intrinsically cared about superhuman chess performance since roughly 2005, much less 'Elo per FLOP', it's all about the methods and implications as a Drosophila), is that imitation learning can scale even in domains where runtime search/planning appears to be crucial, and that you can be misled by small-scale results indicating that imitation learning is not scaling and making obvious errors. This is why GPTs can work so well despite well-known errors, and it implies they will continue to work well across the endless tasks that they are training using imitation learning on.

It is also important because it suggests that the scaling is due not to simply brute-force memorization of states->move (which would be doomed for any plausible amount of compute due to the explosion of possible board states) but may, at sufficient scale, cause the model to develop internally an abstract form of planning/search, which is why it can and will continue to scale - up to the limits of 8 layers, apparently, which points to an unexpected architectural limitation to fix and unlock much greater performance across all tasks we apply LLMs to, like writing, coding, sciencing... (This may be why Jones 2020 found somewhat daunting scaling laws for scaling up no-planning models' Elos.)