Bilevel Optimization of Agent Skills via Monte Carlo Tree Search
Agent \texttt{skills} are structured collections of instructions, tools, and supporting resources that help large language model (LLM) agents perform particular classes of tasks. Empirical evidence shows that the design of \texttt{skills} can materially affect agent task performance, yet systematically optimizing \texttt{skills} remains challenging. Since a \texttt{skill} comprises instructions, tools, and supporting resources in a structured way, optimizing it requires jointly determining both the structure of these components and the content each component contains. This gives rise to a complex decision space with strong interdependence across structure and components. We therefore repr...
| Authors | Chenyi Huang, Haoting Zhang, Jingxu Xu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 17 April 2026 |
| Last updated at source | 17 April 2026 |
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