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
The paper addresses a critical challenge in training large language models for interactive, multi-turn decision-making, which is particularly relevant as AI agents move towards more complex, real-world applications.
Explicit credit assignment and hierarchical reinforcement learning could significantly improve the robustness and effectiveness of AI agents, accelerating their deployment in sophisticated tasks and workflows.
This research provides a more efficient and scalable method for LLMs to learn and operate in long-horizon tasks, potentially overcoming current limitations in sparse and delayed reward environments.
- · AI agent developers
- · Companies implementing AI for complex automation
- · Reinforcement learning researchers
- · Cloud providers supporting LLM training
- · Companies relying on simpler, 'flat' AI policies
- · Manual white-collar workflow providers
More capable and reliable AI agents become available for various applications.
Accelerated automation of white-collar tasks, leading to further productivity gains and workforce restructuring.
Enhanced AI agent capabilities could foster more sophisticated autonomous systems, potentially reshaping economic structures and human-computer interaction paradigms.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG