Process Reward Informed Tree Rollout (PATR) Improves Multi-Turn RL for LLM Agents
A new framework called Process-Scorer Guided Adaptive Tree Rollout (PATR) is proposed for multi-turn reinforcement learning (RL) in large language model (LLM) agents. PATR leverages process feedback to score partial trajectories, selectively branching from promising states and pruning unproductive paths, which enhances exploration efficiency. Experiments on SWE-Bench and FrozenLake show that PATR achieves up to +5.0 and +9.3 point improvements, respectively, over baseline methods.
Why it matters: PATR offers a more efficient approach to training RL agents for complex, long-horizon tasks, potentially advancing the capabilities of LLM-based agents in real-world applications.
Full story at: arXiv Computation and Language ↗