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

1. 1Fine-tune a language model on a dataset of chess moves to improve its ability to predict the best move
2. 2Use reinforcement learning to further improve the model's performance in chess
3. 3Analyze the impact of theoretically-inspired datasets on language model performance in chess
4. 4Evaluate the downstream performance of the model after fine-tuning and reinforcement learning

AI researchers and engineers working on language models and reinforcement learning can benefit from this study, as it provides insights into how to improve reasoning in tasks that are challenging for language models

💡 Fine-tuning a language model to directly predict the best move is crucial for effective reinforcement learning and strong downstream performance

💡 Fine-tuning a language model to predict chess moves leads to strong RL and downstream performance