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

HiPER is an innovative hierarchical reinforcement learning framework designed specifically for training large language model (LLM) agents to perform tasks in long-horizon environments. By explicitly separating high-level planning from low-level execution and introducing the Hierarchical Advantage Estimation (HAE) mechanism, this framework effectively addresses the credit assignment problem across multiple time scales, achieving leading performance on the ALFWorld and WebShop benchmarks.

• Original Author/Maintainer: JonP07
• Source Platform: GitHub
• Original Title: HiPER-agent
• Original Link: https://github.com/JonP07/HiPER-agent
• Source Publication/Update Time: 2026-05-25T14:43:16Z

In the field of artificial intelligence, enabling large language model (LLM) agents to exhibit stable and efficient behavior in complex long-horizon tasks has long been a core challenge for researchers. Traditional reinforcement learning methods often treat an agent's behavior as a flat sequence of actions. This approach struggles with effective credit assignment (i.e., determining which actions contribute most to the final outcome) when faced with complex tasks that require multi-step planning and involve multiple time scales. Especially in long-horizon environments where agents need to perform dozens or even hundreds of steps to complete a task, traditional credit assignment methods fail due to issues like sparse signals and delayed feedback. This is analogous to a student trying to adjust every specific decision in the writing process based only on the final grade—it’s hard to accurately identify which writing strategies are truly effective.

The HiPER (Hierarchical Plan-Execute Reinforcement Learning) framework proposes an innovative solution to the above challenges. Its core design philosophy is to explicitly separate an agent's behavior into two levels: high-level planning and low-level execution, and to design a dedicated Hierarchical Advantage Estimation (HAE) mechanism for this layered structure.

In the HiPER framework, the high-level planning module is responsible for formulating macro task strategies and goal decomposition, while the low-level execution module focuses on converting high-level plans into specific action sequences. This layered design mimics the way humans think about solving complex problems: first conduct global planning, then gradually implement specific actions.

This separation brings several notable advantages:

1. Modular Design: The planning and execution layers can be optimized independently, reducing training complexity
2. Enhanced Interpretability: Researchers can clearly observe the agent's high-level decision-making process
3. Improved Generalization: Learned planning strategies can be transferred to different execution environments

The most innovative contribution of the HiPER framework is the introduction of the Hierarchical Advantage Estimation mechanism. Traditional advantage estimation methods (such as GAE) assume all actions are on the same time scale, while HAE explicitly considers the time scale differences between high-level planning and low-level execution.

Specifically, the HAE mechanism works as follows:

• For high-level planning actions, evaluate their contribution to long-term task goals
• For low-level execution actions, evaluate their completion of the current planning goal
• Transmit credit signals through the layered structure to avoid signal dilution issues in traditional methods

This design makes credit assignment more precise, allowing agents to better understand which planning decisions and execution actions truly drive task progress.

The HiPER framework was evaluated on several challenging benchmarks and achieved impressive results:

ALFWorld is a collection of text-based indoor navigation and object manipulation tasks that require agents to complete tasks like "place the book on the table" in a virtual home environment.

Model	Success Rate
Qwen2.5-1.5B-Instruct	95.3%
Qwen2.5-7B-Instruct	97.4%