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adamkarvonen / chess_llm_interpretability

Visualizing the internal board state of a GPT trained on chess PGN strings, and performing interventions on its internal board state and representation of player Elo.

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Can you try to incorporate self-play technology similar to AlphaGo Zero?

win10ogod opened this issue · comments

commented

Can you try to incorporate self-play technology similar to AlphaGo Zero?

commented

For now, my goal is more focused on the interpretability of an LLM trained to play chess, rather than making a better chess LLM. When making a better chess LLM I would first explore using a larger model trained on more data with more compute, and identifying which one of those factors is the current Elo bottleneck. This would probably be more compute efficient than a self-play approach. We have an effectively unlimited amount of human chess games data, and an existence proof in GPT-3.5-turbo-instruct that an LLM can play at 1800 Elo off of human chess games.

However, self play with LLMs could also be interesting as well. I currently have no plans of exploring this, but anyone is welcome to try.