Hierarchical Reinforcement Learning with Augmented Step-Level Transitions for LLM Agents

arXiv:2604.05808v2 Announce Type: replace Abstract: Large language model (LLM) agents have demonstrated strong capabilities in complex interactive decision-making tasks. However, existing LLM agents typically rely on increasingly long interaction histories, resulting in high computational cost and l