Grounded Chess Reasoning in Language Models via Master Distillation

The paper introduces a framework called Master Distillation for enhancing language models' reasoning capabilities in specialized domains, exemplified by a 4B parameter model named C1 applied to chess. C1 achieved 48.1% accuracy, outperforming existing open-source and proprietary models, while generating explanations with significantly fewer tokens than baseline methods. This approach captures the full reasoning process of expert systems, enabling compact models to produce transparent, explainable solutions, which is crucial for practitioners seeking to integrate grounded reasoning in AI applications.