HiPER: Hierarchical Reinforcement Learning with Explicit Credit Assignment for Large Language Model Agents
#Hierarchical Reinforcement Learning #Large Language Model Agents #Explicit Credit Assignment #Sparse Rewards #Multi‑Turn Decision Making #LM Policy Decomposition
📌 Key Takeaways
- HiPER introduces a two‑level hierarchical policy for LLM agents.
- Explicit credit assignment links high‑level goals to low‑level actions.
- Designed to overcome long‑horizon, sparse‑reward challenges.
- Framework is compatible with existing large language model architectures.
- Preliminary results on simulated multi‑turn tasks show more stable learning.
📖 Full Retelling
Researchers propose *HiPER*—a Hierarchical Reinforcement Learning framework that introduces explicit credit assignment mechanisms for Language Model (LM) agents. The study appears on arXiv (2602.16165v1) and was posted on 26 Feb 2026. The authors aim to enable large language model agents to tackle multi‑turn decision‑making tasks that demand long‑horizon planning and operate under sparse, delayed rewards, a setting where conventional flat RL policies that pick a single action at each step struggle.
HiPER decomposes the decision process into a high‑level policy that selects sub‑goals or plans and a low‑level policy that executes concrete actions. By explicitly attributing reward signals to the appropriate levels of the hierarchy, the framework alleviates the credit‑assignment problem that usually hampers learning efficiency in long‑horizon, sparse‑reward scenarios. The preprint outlines how this method can be integrated with existing language model architectures and discusses initial experiments that indicate improved learning stability and performance on benchmark dialogue‑style tasks.
🏷️ Themes
Reinforcement Learning, Hierarchical Control, Credit Assignment, Large Language Models, Long‑Horizon Decision Making, Sparse Rewards
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Original Source
arXiv:2602.16165v1 Announce Type: 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 policies operating at a single time scale, selecting one action at each turn. In sparse-reward settings, such flat polici
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