腾讯发布SkillAdam提升智能体指令优化效率
腾讯新论文教你如何让AI智能体自我优化指令，既省钱又高效，比之前方法准确率高6.6%。
腾讯研究团队提出SkillAdam方法，通过记录历史修复和避免大幅重写来优化智能体指令。该方法在购物和旅行规划任务中达到28.3%准确率，比之前最佳方法SkillOpt高出6.6个百分点。同时，SkillAdam的token消耗量仅为SkillOpt的三分之一。
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"
