Reasoning Through Chess: How Reasoning Evolves from Data Through Fine-Tuning and Reinforcement Learning

• URL: http://arxiv.org/abs/2604.05134v1
• Date: Mon, 06 Apr 2026 19:53:39 GMT
• Title: Reasoning Through Chess: How Reasoning Evolves from Data Through Fine-Tuning and Reinforcement Learning
• Abstract summary: We analyze how a set of theoretically-inspired datasets impacts language model performance in chess.<n>We find that fine-tuning a model to directly predict the best move leads to effective RL and the strongest downstream performance.<n>We show that RL induces a substantial positive shift in the distribution of move quality and reduces hallucination rates as a side effect.
• Score: 7.920254637344918
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: How can you get a language model to reason in a task it natively struggles with? We study how reasoning evolves in a language model -- from supervised fine-tuning (SFT) to reinforcement learning (RL) -- by analyzing how a set of theoretically-inspired datasets impacts language model performance in chess. We find that fine-tuning a model to directly predict the best move leads to effective RL and the strongest downstream performance -- however, the RL step elicits unfaithful reasoning (reasoning inconsistent with the chosen move). Alternatively, training on multi-move trajectories yields comparable downstream performance with faithful reasoning and more stable RL. We show that RL induces a substantial positive shift in the distribution of move quality and reduces hallucination rates as a side effect. Finally, we find several SFT-checkpoint metrics -- metrics spanning evaluation performance, hallucination rates, and reasoning quality -- to be predictive of post-RL model performance. We release checkpoints and final models as well as training data, evaluations, and code which allowed us to surpass leading open-source reasoning models in chess with a 7B-parameter model.

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