让大模型智能体用更少计算实现更好决策。
Hierarchical Reinforcement Learning with Augmented Step-Level Transitions for LLM Agents
- 分层强化学习，只看单步交互而非完整历史。
- 在科学世界和阿尔福世界任务中表现更优，节省30%以上令牌。
- 适合需要高效推理的大模型应用开发人员。
大型语言模型（LLM）智能体在复杂交互式决策任务中展现出强大能力。然而，现有方法通常依赖不断增长的交互历史，导致计算成本高且难以扩展。本文提出STEP-HRL，一种分层强化学习框架，通过仅基于单步转移进行步级学习，降低对历史信息的依赖。该框架采用层次化任务结构，用已完成的子任务表示全局进展；并通过局部进度模块，迭代选择性地总结每个子任务内的交互历史，生成紧凑的局部进展摘要。这些设计共同构建了高低层策略均适用的增强型步级转移。在ScienceWorld和ALFWorld基准上的实验表明，STEP-HRL在性能与泛化能力上显著优于基线方法，同时减少超过30%的令牌使用量。代码已开源：https://github.com/TonyStark042/STEP-HRL。
原文摘要 · Abstract (English)
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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