arXiv AI October 16, 2026 By Xiuhui Zhang, Ning Gao, Xingyu Jiang, Yihui Chen, Yuheng Pan, Mohan Zhang, Yue Deng

Progress Reward Model for Reinforcement Learning via Large Language Models

The paper introduces PRM4RL, a framework that combines task planning and dense reward generation using large language models for reinforcement learning. It decomposes complex tasks into manageable subtasks and employs a fine‑grained progress function to monitor execution, which is then used to construct a Progress Reward Model for potential‑based reward shaping. Experiments on robotics control tasks show that PRM4RL outperforms existing LLM‑based planning and reward methods, achieving state‑of‑the‑art performance.

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