Learning and Reusing Policy Decompositions for Hierarchical Generalized Planning with LLM Agents
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866913101122633728 |
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| author | Sohrabi, Shirin Ananthakrishnan, Haritha Kokel, Harsha Srinivas, Kavitha Katz, Michael |
| author_facet | Sohrabi, Shirin Ananthakrishnan, Haritha Kokel, Harsha Srinivas, Kavitha Katz, Michael |
| contents | 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 components from successful executions, organizing them into a component library for compositional policy generation. We address three challenges: (1) learning components through automated decomposition, (2) generalizing components 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_06957 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Learning and Reusing Policy Decompositions for Hierarchical Generalized Planning with LLM Agents Sohrabi, Shirin Ananthakrishnan, Haritha Kokel, Harsha Srinivas, Kavitha Katz, Michael Artificial Intelligence 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 components from successful executions, organizing them into a component library for compositional policy generation. We address three challenges: (1) learning components through automated decomposition, (2) generalizing components 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. |
| title | Learning and Reusing Policy Decompositions for Hierarchical Generalized Planning with LLM Agents |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2605.06957 |