腾讯论文提出 SkillAdam，通过让改写 AI 记住过往修复并避免大幅重写，实现智能体技能指令的自动优化。在长程购物与旅行规划任务上，其平均准确率达 28.3%，优于此前最佳方法 SkillOpt 的 21.7%，且 token 消耗约为后者的三分之一。该方法旨在解决自动改写技能时反复循环、浪费 token 的问题。
New Tencent paper letting AI auto-improve your agent's instructions works better and costs far less if it remembers past fixes and avoids big, risky rewrites.
Agent skills are instruction files that teach an agent how to do a job. Tools that auto-rewrite them often go in circles, burning tokens as new edits undo fixes that already worked.
SkillAdam teaches the rewriting AI 2 habits. It keeps a log of what's been fixed, and it makes smaller changes when results are mixed.
On long shopping and travel planning tasks, it scored 28.3% average accuracy versus 21.7% for SkillOpt, the best earlier method. It also used about a third as many tokens.
If you auto-tune your agent's instructions, give the process a memory of past fixes and a brake on big edits.
– arxiv. org/abs/2609.08944
Title: "SkillAdam: Stable and Efficient Skill Evolution for Agents"
来源：Rohan Paul · x.com