Tagged #Chess

I trained a GPT-2 model on Stockfish self-played games in the most naive way, with no search, and it can play decently. The model is trained to output the next move given the FEN string of the board (single state). While I present some gotchas and caveats, the results are quite acceptable for the amount of work and computing invested. I also present a basic attention visualiser parsing the attention of the text tokens into the board.

We propose contrastive sparse autoencoders (CSAE), a novel framework for studying pairs of game trajectories. Using CSAE, we are able to extract and interpret concepts that are meaningful to the chess-agent plans. We primarily focused on a qualitative analysis of the CSAE features before proposing an automated feature taxonomy. Furthermore, to evaluate the quality of our trained CSAE, we devise sanity checks to wave spurious correlations in our results.