Chessformer: A Unified Architecture for Chess Modeling

Chess has played a uniquely important historical role as a testbed domain for artificial intelligence. Applying new architectures to improve absolute chess performance, and more recently to predict human moves at specified skill levels, has therefore garnered attention in the machine learning literature. Current approaches to these problems employ transformer models with widely varying architectural designs, and use unintuitive tokenization schemes that are not amenable to interpretability techniques, which hinders their applicability for teaching and human-AI interaction. We introduce Chessformer, a novel chess transformer model design that consists of an encoder-only model which processes chessboard squares as input tokens, instead of moves or the entire position, a dynamic positional encoding scheme that allows the model to flexibly adapt to the unique geometries present in chess, and an attention-based policy output design. We show that Chessformer advances the state of the art in all three major chess modeling goals: it significantly improves the chess-playing performance of a state-of-the-art chess engine, it surpasses the previous best human move-matching prediction performance with a much smaller model, and it enables substantial interpretability benefits. Our unified approach constitutes a broad advance across several important tasks in chess AI, and also demonstrates the benefits of carefully adapting transformers' tokenization systems, output systems, and positional encodings to reflect the structure of a domain of interest.