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        <title>训练 - 标签 - vllbc02&#39;s blogs</title>
        <link>https://blog.vllbc.top/tags/%E8%AE%AD%E7%BB%83/</link>
        <description>训练 - 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>Sat, 06 Jun 2026 00:00:00 &#43;0000</lastBuildDate><atom:link href="https://blog.vllbc.top/tags/%E8%AE%AD%E7%BB%83/" rel="self" type="application/rss+xml" /><item>
    <title>MTP（Multi-Token Prediction）</title>
    <link>https://blog.vllbc.top/mtpmulti-token-prediction/</link>
    <pubDate>Sat, 06 Jun 2026 00:00:00 &#43;0000</pubDate>
    <author>vllbc</author>
    <guid>https://blog.vllbc.top/mtpmulti-token-prediction/</guid>
    <description><![CDATA[<h1 id="mtpmulti-token-prediction">MTP（Multi-Token Prediction）</h1>
<blockquote>
<p>来自 Sebastian Raschka 的 <em>The Big LLM Architecture
Comparison</em>。一个<strong>训练阶段</strong>的多 token 预测技巧。</p>
</blockquote>
<h2 id="一句话">一句话</h2>
<p>传统 LLM 每个位置只预测下一个 token（next-token prediction）。MTP
在每个位置加几个小的 head（线性层），同时输出 t+1..t+k 的
logits，对所有偏移的 cross-entropy 求和（论文推荐 k=4）。</p>]]></description>
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