The authors build an LLM-based agent system that learns reusable task decompositions: when the agent successfully completes a task, it automatically extracts reusable components (sub-policies) and stores them in a library for future use via semantic search. This combines classical planning ideas (hierarchical task decomposition, generalized policies) with LLM agents. On the AppWorld benchmark (tasks involving real app interactions), their approach achieves 98.2% accuracy on normal tasks and 97.8% on challenge tasks with unseen apps, improving 15.8 points over static approaches on hard cases; for open-source models, dynamic component reuse enables 62.5% success versus near-zero without it.
Main takeaways:
- The system learns parameterized policies that generalize across task instances and automatically decomposes successful executions into reusable components
- Components are stored in a library and retrieved via semantic search when facing new tasks
- Addresses three challenges: automated decomposition of successful trajectories, generalizing components to maximize reuse, and efficient retrieval
- On AppWorld benchmark with unseen applications, achieves 97.8-98.2% accuracy (15.8 point improvement over static synthesis on challenging scenarios)
- For weaker open-source models, component reuse is the difference between 62.5% success and near-zero