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

arXiv:2602.16165v2 Announce Type: replace 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 policies must propagate credit across the entire trajectory without explicit temporal abstraction, which

Source: arXiv cs.LG — read the full report at the original publisher.

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