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"

Oct 2, 2026 · 10:53 AM UTC

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Replying to @rohanpaul_ai
smart naming: the brake on big edits is basically adam's adaptive step size. did the paper ablate the log vs the brake separately?
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Replying to @rohanpaul_ai
past fixes staying in-context vs a separate memory changes how much you can accumulate before it degrades. which path did they take?
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Replying to @rohanpaul_ai
Interesting direction. The hard part of self-improving AI may not be making changes - it’s knowing which changes actually made the system better.
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