Tree Search Distillation for Language Models Using PPO
Summary
An experimental blog post that combines Monte Carlo Tree Search with Tree-of-Thoughts and online PPO distillation to improve language-model reasoning, tested on Countdown with a 1.5B model. The MCTS-distilled policy achieves 11.3% mean@16, outperforming CISPO and best-of-N baselines, with detailed implementation notes and infrastructure. The piece discusses scalability, reward design, and suggests promising directions for applying search-based reasoning to real-world AI-driven automation tasks.