Hierarchical World-Model Reinforcement Learning for Long-Horizon Reasoning in Large Language Model Agents
Keywords:
hierarchical reinforcement learning, world models, large language models, long-horizon reasoning, agent architecture, AI governance, fairness, robustnessAbstract
Large language model agents have demonstrated remarkable capabilities in language understanding and generation, yet they remain fundamentally limited in tasks requiring extended sequential reasoning and planning over long horizons. This paper proposes a framework that integrates hierarchical reinforcement learning with learned world models to address these limitations. By coupling a high-level abstract planner with a low-level world-model simulator, the agent can decompose complex long-horizon tasks into manageable subgoals, evaluate hypothetical action sequences in a learned internal model, and refine its reasoning through recursive credit assignment. The paper examines architectural trade-offs between abstraction granularity and model fidelity, discusses training stability and sample efficiency challenges, and explores the socio-technical implications of deploying such systems in critical infrastructure. Key considerations include robustness against distributional shift, fairness in reward design, computational sustainability, and the need for transparent governance mechanisms. The hierarchical world-model approach offers a principled path toward more deliberative and scalable LLM agents, but raises important questions about safety, accountability, and alignment in autonomous decision-making systems. The paper concludes with recommendations for future research and policy development.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
