HiPER: Hierarchical Reinforcement Learning with Explicit Credit Assignment for Large Language Model Agents 事件

HiPER: Hierarchical Reinforcement Learning with Explicit Credit Assignment for Large Language Model Agents arXiv:2602.16165v2 Announce Type: replace-cross Abstract: Training LLMs as interactive agents for multi-turn decision-making remains challenging, particularly in long-horizon tasks with sparse and delayed rewards, where agents must execute extended sequences of actions before receiving meaningful feedback. Most existing reinforcement learning (RL) approaches model LLM agents as flat polici