Researchers have developed a novel method for training agent skills, treating them as model weights rather than manually writing them. This approach, inspired by Microsoft's SkillOpt paper, involves a student model performing tasks and an optimizer model proposing edits to a skill file based on performance. A validation gate then scores these proposed edits on selection tasks, only accepting changes that improve performance. This method was tested on a browsing skill using Opus 5.5 and Claude Fable 5.1, and a starter skill with Claude Haiku and Sonnet, demonstrating its potential to refine agent behavior and codify house conventions. AI
IMPACT This method could streamline the development of AI agents by automating skill refinement, potentially leading to more robust and efficient agent behaviors.
RANK_REASON The item describes a novel method for training AI agent skills, drawing parallels to model weight training and referencing a specific research paper. [lever_c_demoted from research: ic=1 ai=1.0]
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