Bilevel Optimization of Agent Skills via Monte Carlo Tree Search

This research introduces a bilevel optimization framework for systematically enhancing "agent skills" in large language model (LLM) agents. It uses an outer loop of Monte Carlo Tree Search to jointly optimize the structure and content of these skills, addressing a complex decision space for improved task performance.