<rss xmlns:atom="http://www.w3.org/2005/Atom" version="2.0">
    <channel>
        <title>LLM/Post-Training - 标签 - vllbc02&#39;s blogs</title>
        <link>https://blog.vllbc.top/tags/llm/post-training/</link>
        <description>LLM/Post-Training - 标签 - vllbc02&#39;s blogs</description>
        <generator>Hugo -- gohugo.io</generator><language>zh-CN</language><copyright>This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.</copyright><lastBuildDate>Mon, 03 Aug 2026 00:00:00 &#43;0000</lastBuildDate><atom:link href="https://blog.vllbc.top/tags/llm/post-training/" rel="self" type="application/rss+xml" /><item>
    <title>SFT、RL 与 OPD：从分布视角理解后训练</title>
    <link>https://blog.vllbc.top/sftrl-%E4%B8%8E-opd%E5%88%86%E5%B8%83%E8%A7%86%E8%A7%92/</link>
    <pubDate>Mon, 03 Aug 2026 00:00:00 &#43;0000</pubDate>
    <author>wh.</author>
    <guid>https://blog.vllbc.top/sftrl-%E4%B8%8E-opd%E5%88%86%E5%B8%83%E8%A7%86%E8%A7%92/</guid>
    <description><![CDATA[<h1 id="sftrl-与-opd从分布视角理解后训练">SFT、RL 与 OPD：从分布视角理解后训练</h1>
<blockquote>
<p>[!info] TL;DR
<strong>核心思想</strong>：把语言模型看成序列分布，post-training 就是在重塑这个分布。SFT 追随固定的外部数据分布，RL 从当前策略采样并用奖励筛选，OPD 在学生自己的状态上匹配 teacher 分布。</p>]]></description>
</item>
</channel>
</rss>
