微软 TeleTune：从离线日志进化智能体技能

Rohan Paul· @rohanpaul_ai · X·2026-10-09 21:00· 17 分钟前AI 评分42

AI 导读

微软论文提出 TeleTune，让智能体直接从原始使用日志中学习软件技能，无需实时测试环境，仅保留能更好预测用户下一步动作的技能编辑。该方法猜测每个会话的目标，用文本技能库让模型预测每条日志动作，错误预测触发技能库修改，只有在留出日志上准确率提升的编辑才会被保留。论文称旧日志上的下一步动作准确率与实时成功率一致，建议产品记录用户活动并据此挖掘智能体技能。

正文

New Microsoft paper shows that agents can learn software skills from raw usage logs by keeping only skill edits that better predict users' next actions.

i.e. You do not need a live test environment to check whether a new agent skill helps, because next-action accuracy on old logs tracked live success.

Usage logs hold lots of know-how, but they record no goals, often mix several tasks, and cannot be replayed. Earlier methods, like Agent Workflow Memory, need goal-labeled examples or a live environment to test changes.

TeleTune guesses each session's goal and has the model predict every logged action using a text skill library. Wrong guesses suggest library edits, and an edit stays only if accuracy rises on held-out logs.

If your product records user activity, mine it for agent skills and judge each change by next-action accuracy on held-out logs.

– arxiv. org/abs/2610.05437

Title: "TeleTune: Evolving Agent Skills From Offline Telemetry"

来源：Rohan Paul · x.com