腾讯发布SkillAdam方法,通过让AI记录过往修复、避免高风险大幅改写,自动优化智能体技能指令。在长购物与旅行规划任务中,SkillAdam平均准确率达28.3%,优于此前最优方法SkillOpt的21.7%,token用量约为后者的三分之一。
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"
来源:Rohan Paul · x.com