Learning and Reusing Policy Decompositions for Hierarchical Generalized Planning with LLM Agents

We present a dynamic policy-learning approach that combines generalized planning and hierarchical task decomposition for LLM-based agents. Our method, Hierarchical Component Learning for Generalized Policies (HCL-GP), learns parameterized policies that generalize across task instances and automatically extracts reusable policy components from successful executions, organizing them into a skill library for compositional policy generation. We address three challenges: (1) learning skills through automated decomposition, (2) generalizing skills to maximize reuse, and (3) efficient retrieval via semantic search.

Evaluated on the AppWorld benchmark, our approach achieves 98.2% accuracy on normal tasks and 97.8% on challenge tasks with unseen applications, improving 15.8 points over static synthesis on challenging scenarios. For open-source models, dynamic reuse enables 62.5% success versus near-zero without reuse. This demonstrates that classical planning concepts can be effectively integrated with LLM agents for improved accuracy and efficiency.