Tree-Aware Posterior Distillation for Long-Horizon Agentic Reinforcement Learning

Reinforcement learning with verifiable rewards has become a scalable post-training paradigm for large language models. However, training agents over long-horizon interactions presents two challenges: using only terminal rewards makes turn-level credit assignment sparse and high-variance, while seque