Conditioning ChessGPT via Prompt Tuning

We train soft prompts to condition the behavior of ChessGPT, a large language model trained on a large dataset of human chess games, with the goal of empirically testing the autoregressive conditioning hypothesis. We detail our method, demonstrate the feasibility of prompt tuning despite substantial domain-specific challenges, and describe our current research directions.