CHSM8: A Chess Transformer that Follows Natural-Language Style Prompts

Key points

• The model is pretrained without search on 1.07B filtered Lichess games, using a factorized move representation and legal-move masking.1
• We then fine-tune it to follow text on a synthetic dataset: Gemma-4-31B describes the playing style of 3M game sides, a 1B Gemma-3 model distilled from the 2.4M of those descriptions that contradict nothing on the board labels the rest, and every one of 30M selected game sides gets five descriptions.1
• Supervised fine-tuning attaches a frozen text encoder through gated cross-attention, and direct preference optimization (DPO) uses the same moves under a mismatched description as the rejected sample.1
• After DPO, style adherence rises on every prompt we test (for example, queens traded early 53% -> 67%, pawn moves 29% -> 32% of all moves, queenside castling 97.3% -> 99.7%), and strength without a prompt stays within noise of SFT (1792 vs. 1797 Elo).1

Sources (1)

• [1]CHSM8: A Chess Transformer that Follows Natural-Language Style Prompts
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 03:57 PM

  We present CHSM8, a 210M-parameter chess transformer that plays moves conditioned on a free-text description of a playing style, e.g. "go for the Sicilian Defense", "solid, safe, risk-free chess" or "You are Magnus Carlsen in his prime".

  The model is pretrained without search on 1.07B filtered Lichess games, using a factorized move representation and legal-move masking.

Extractive summary: sentences quoted from the sources.