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

Shuai Zhen; Yanhua Yu; Ruopei Guo; Nan Cheng; Yang Deng

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arXiv · 2604.05808

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

Shuai Zhen·Yanhua Yu·Ruopei Guo·Nan Cheng·Yang Deng

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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 limited scalability. In this paper, we propose STEP-HRL, a hierarchical reinforcement learning (HRL) framework that enables step-level learning by conditioning only on single-step transitions rather than full interaction histories. STEP-HRL structures tasks hierarchically, using completed subtasks to represent global progress of overall task. By introducing a local progress module, it also iteratively and selectively summarizes interaction history within each subtask to produce a compact summary of local progress. Together, these components yield augmented step-level transitions for both high-level and low-level policies. Experimental results on ScienceWorld and ALFWorld benchmarks consistently demonstrate that STEP-HRL substantially outperforms baselines in terms of performance and generalization while reducing token usage. Our code is available at https://github.com/TonyStark042/STEP-HRL.

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Shuai Zhen, Yanhua Yu, Ruopei Guo, Nan Cheng, Yang Deng. 2026-04-07. Hierarchical Reinforcement Learning with Augmented Step-Level Transitions for LLM Agents. https://arxiv.org/abs/2604.05808

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