Tree Search Distillation for Language Models Using PPO

Tree search distillation for language models using PPO achieves a 15% improvement in reasoning accuracy over standard supervised fine-tuning, as demonstrated on GSM8K math problems. The method guides models through 100 exploration steps per query, producing 3x more diverse solutions than# greedy decoding. Outperforms #2 in algorithmic novelty, applying reinforcement learning to structure outputs, not just detect patterns. This technique reduces training time by 25% compared to Monte Carlo tree search baselines, making it faster than the average optimization approach for large language models.