Reasoning Through Chess: How Reasoning Evolves from Data Through Fine-Tuning and Reinforcement Learning
📰 ArXiv cs.AI
Fine-tuning a language model to predict the best move in chess leads to effective reinforcement learning and strong downstream performance
Action Steps
- Fine-tune a language model on a dataset of chess moves to improve its ability to predict the best move
- Use reinforcement learning to further improve the model's performance in chess
- Analyze the impact of theoretically-inspired datasets on language model performance in chess
- Evaluate the downstream performance of the model after fine-tuning and reinforcement learning
Who Needs to Know This
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
Key Insight
💡 Fine-tuning a language model to directly predict the best move is crucial for effective reinforcement learning and strong downstream performance
Share This
💡 Fine-tuning a language model to predict chess moves leads to strong RL and downstream performance
Key Takeaways
Fine-tuning a language model to predict the best move in chess leads to effective reinforcement learning and strong downstream performance
Full Article
Title: Reasoning Through Chess: How Reasoning Evolves from Data Through Fine-Tuning and Reinforcement Learning
Abstract:
arXiv:2604.05134v1 Announce Type: cross 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, th
Abstract:
arXiv:2604.05134v1 Announce Type: cross 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, th
DeepCamp AI