Innovation
Methods, ideas, or system contributions that make the work stand out.
Chess-Language Model
Encoder-Decoder Architecture
Iterative Distillation
Natural Language Bellman Update
Cross-Attention
Language Models that Play Chess and Explain Their Moves
🤖 AI Summary
This study addresses the lack of interpretability in chess engines and the insufficient playing strength of language models by proposing Queen, a novel model that pioneers an encoder-decoder architecture. Specifically, Queen integrates an expert encoder with a language model via cross-attention mechanisms, trained through instruction tuning, question-answering curriculum learning, and a natural-language-adapted Bellman iteration distillation algorithm. Experimental results demonstrate that Queen achieves an Elo rating improvement exceeding 900 points, reaching grandmaster-level performance. Furthermore, the model generates explanations whose quality closely approximates that of top-tier large language models while substantially reducing parameter count. Ultimately, this work effectively unifies high-level gameplay decision-making with natural language interpretability within a single, efficient framework.
📝 Abstract
Modern chess engines are silent experts: they play at a superhuman level, but do not offer explanations for their play. On the other hand, language models (LMs) can generate plausible-sounding explanations, but their weak playing strength limits the utility of their explanations. We introduce Queen, a 4B-parameter chess-language model that can explain its moves and plans while playing at the level of a typical Grandmaster. Our novel framework enables domain-specific reasoning through complementary components: an encoder-decoder architecture and an iterative distillation algorithm. This architecture integrates a silent expert chess encoder with an instruction-tuned LM through cross-attention, which we train via a question-answering curriculum to extract chess concepts from the encoder's representations. Building on this domain-adapted model, we iteratively improve its explanations with a natural-language analog of the Bellman update: the model analyzes the positions after its top candidate moves and consolidates them into an explanation of the current position, which is then distilled back into the model. Over seven iterations, our model gains over 900 Elo points (1782 to 2697), substantially surpassing all frontier models on both playing strength and puzzle accuracy, despite containing three orders of magnitude fewer parameters. Furthermore, LM-based evaluations show that our explanations are fluent and approach GPT-5.6-Sol (high) in coherence. The generality of our architecture and training procedure suggests a recipe for applying language models to domains where silent expert encoders are available, like games, robotics, and computer use.
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