<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Skill-Distillation on All about Raspberry Pi</title><link>https://hugozhu.site/tags/skill-distillation/</link><description>Recent content in Skill-Distillation on All about Raspberry Pi</description><generator>Hugo</generator><language>en</language><lastBuildDate>Tue, 09 Jun 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://hugozhu.site/tags/skill-distillation/index.xml" rel="self" type="application/rss+xml"/><item><title>自动优化 Agent 的执行轨迹</title><link>https://hugozhu.site/post/2026/262-auto-optimize-agent-trajectory/</link><pubDate>Tue, 09 Jun 2026 00:00:00 +0000</pubDate><guid>https://hugozhu.site/post/2026/262-auto-optimize-agent-trajectory/</guid><description>&lt;p&gt;上个月有人问我一个问题：「我已经有 LLM-as-Judge 做 eval 了，能不能用它来自动优化 Agent 的执行路径？在不降质量的前提下，找到最省钱的轨迹，然后让 Agent 记住？」&lt;/p&gt;
&lt;p&gt;这个问题的答案值得展开。答案是能，而且这可能是当前 Agent 优化里最值得投入的方向。但大多数团队理解错了「优化」的对象。&lt;/p&gt;
&lt;p&gt;&lt;a href="https://hugozhu.site/img/2026/auto-optimize-agent-trajectory.png"&gt;&lt;img src="https://hugozhu.site/img/2026/auto-optimize-agent-trajectory-thumb.jpg" alt="Agent 轨迹优化：从零规划到 Skill 蒸馏"&gt;&lt;/a&gt;&lt;/p&gt;</description></item></channel></rss>