Retrieval-Augmented Skill Optimization via Cross-Harness Adaptation· 通过跨框架适应增强检索技能优化
An agent skill is a reusable, actionable natural-language artifact that guides an agent to perform a task effectively under a given harness. Recent studies have explored the optimization of agent skills, contributing to a growing collection of publicly available skills spanning diverse tasks, domains, and harnesses. Despite millions of publicly shared skills, existing skill optimization methods largely overlook this accumulated knowledge, instead relying solely on expensive agent rollouts to iteratively refine skills for a target task. To address this, we propose \textbf{Retrieval-Augmented Skill Optimization (RASO)}, a framework that leverages an external skill corpus as prior knowledge throughout skill optimization. RASO retrieves relevant knowledge from existing skills and adapts it to
通过跨框架适应增强检索技能优化，利用外部技能库优化代理技能。
- 核心方法
- RASO框架通过从外部技能库中检索相关知识并适应目标任务的框架，利用先验知识优化代理技能。
- 适合谁读
- 研究者 / 工程师
- 要解决的问题
- 现有的代理技能优化方法忽略了大量公开可用的技能知识，导致优化过程成本高昂且效率低下。
- 关键实验
- 未提供
- 主要贡献
- 提出了RASO框架，有效利用已有技能库，降低技能优化成本，提高优化效率。
- 意义与局限
- RASO框架能够显著减少优化所需资源，促进技能的跨任务和跨领域应用，但可能受限于外部技能库的质量和适用性。