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        <title>Sao - 标签 - vllbc02&#39;s blogs</title>
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        <description>Sao - 标签 - vllbc02&#39;s blogs</description>
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    <title>GLM-5.3 阅读笔记 — 后训练 scaling、SAO 算法与 Slime 框架</title>
    <link>https://blog.vllbc.top/glm-5.3-%E5%90%8E%E8%AE%AD%E7%BB%83scaling%E4%B8%8Esao%E7%AE%97%E6%B3%95/</link>
    <pubDate>Mon, 17 Aug 2026 00:00:00 &#43;0000</pubDate>
    <author>知乎专栏《deep research》</author>
    <guid>https://blog.vllbc.top/glm-5.3-%E5%90%8E%E8%AE%AD%E7%BB%83scaling%E4%B8%8Esao%E7%AE%97%E6%B3%95/</guid>
    <description><![CDATA[<h1 id="glm-53--后训练-scaling-与-sao-算法">GLM-5.3 — 后训练 scaling 与 SAO 算法</h1>
<blockquote>
<p>[!info] TL;DR
<strong>核心思想</strong>：复用基座，把资源堆到<strong>后训练</strong>上。GLM-5.3 的全部提升（自研 Z.ai Code Bench +50%）来自后训练 pipeline 的三处扩展——<strong>算法</strong>（SAO 单轨迹异步 RL 替代 GRPO）、<strong>框架</strong>（Slime 异步训练框架）、<strong>环境</strong>（百万级可执行 sandbox）。</p>]]></description>
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