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<!DOCTYPE html>
<html lang="zh-CN">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Training - AI</title>
<link rel="stylesheet" href="styles.css">
<style>
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</style>
</head>
<body>
<div class="container">
<div class="sidebar">
<div class="logo">📚 索引</div>
<ul class="toc">
<li><a href="index.html">🏠 首页</a></li>
<li><a href="index.html#ai">🤖 AI</a>
<ul>
<li><a href="ai-model.html">1. Model</a>
<ul>
<li><a href="ai-model-gpt-principles.html">GPT 模型原理</a></li>
<li><a href="ai-model-attention-mechanism.html">注意力机制</a></li>
</ul>
</li>
<li><a href="ai-posttraining.html">Training</a>
<ul>
<li><a href="ai-pretraining.html">Pre-training</a></li>
<li><a href="ai-posttraining-overview.html">Post-training 全景指南</a></li>
<li><a href="ai-posttraining-peft.html">PEFT 详解</a></li>
</ul>
</li>
<li><a href="ai-agent.html">4. Agent</a>
<ul>
<li><a href="ai-agent-llm-survey.html">LLM Agent Survey</a></li>
<li><a href="ai-agent-agentic-reasoning.html">Agentic Reasoning</a></li>
<li><a href="ai-agent-memory.html">Memory</a></li>
<li><a href="ai-agent-self-evolving.html">Self-Evolving</a></li>
<li><a href="ai-agent-multi-agent.html">Multi-Agent Systems</a></li>
<li><a href="ai-agent-agentic-rl.html">Agentic RL</a></li>
<li><a href="ai-agent-knowledge-graph.html">Knowledge Graph</a></li>
<li><a href="ai-agent-rag.html">RAG</a></li>
<li><a href="ai-agent-tree-of-thoughts.html">Tree of Thoughts</a></li>
<li><a href="ai-agent-function-calling.html">Tools</a></li>
</ul>
</li>
</ul>
</li>
<li><a href="decision.html">🔄 端到端</a></li>
<li><a href="models.html">⏱️ 预测</a></li>
</ul>
</div>
<main class="content">
<header class="header">
<h1>Training</h1>
<p class="subtitle">Pre-training + Mid-training + Post-training 全流程解析</p>
</header>
<div class="breadcrumb">
<a href="index.html">首页</a> > <a href="index.html#ai">AI</a> > Training
</div>
<div class="page-toc">
<h4 style="margin-bottom: 15px; color: #1e40af;">📑 页面目录</h4>
<ul style="list-style: none; padding: 0; margin: 0;">
<li style="margin-bottom: 8px;"><a href="#training" style="color: #1e40af; text-decoration: none; font-weight: 600;">Training</a></li>
<li style="margin-left: 20px; margin-bottom: 6px;"><a href="#pre-training" style="color: #7c3aed; text-decoration: none; font-size: 0.95rem;">Pre-training</a></li>
<li style="margin-left: 20px; margin-bottom: 6px;"><a href="#mid-training" style="color: #7c3aed; text-decoration: none; font-size: 0.95rem;">Mid-training</a></li>
<li style="margin-left: 20px; margin-bottom: 6px;"><a href="#post-training" style="color: #7c3aed; text-decoration: none; font-size: 0.95rem;">Post-training</a></li>
</ul>
</div>
<section class="ai-section">
<h2 id="training">Training</h2>
<p>大语言模型训练全流程:Pre-training → Mid-training → Post-training</p>
<h3 id="pre-training">Pre-training</h3>
<p>预训练是 LLM 训练的第一阶段,通过海量无标注文本数据让模型学习语言规律和世界知识。模型在大量语料上进行自监督学习(如 Next Token Prediction),产出一个"什么都知道一点、但什么都不太好用"的基座模型。</p>
<div class="article-grid">
<a href="ai-pretraining.html" class="article-card-link">
<div class="article-card">
<h3>Pre-training</h3>
<p>系统学习预训练阶段的关键技术与方法,包括 tokenization、BPE 算法等基础知识。</p>
</div>
</a>
</div>
<h3 id="mid-training">Mid-training</h3>
<p>训练与领域适应是连接预训练与后训练的中间阶段。在预训练完成后,通过持续训练(Continue Training)在特定领域数据上进一步优化模型,使其在特定领域(如代码、数学、科学)表现更好。Mid-training 通常使用比预训练更小但更高质量的数据集。</p>
<p>相关技术包括:领域自适应预训练(Domain-Adaptive Pretraining)、任务导向的持续训练等。这一阶段在 DeepSeek、Codex 等专业模型中尤为关键。</p>
<h3 id="post-training">Post-training</h3>
<p>后训练是将基座模型"精装修"为可用产品的关键阶段,涵盖监督微调(SFT)、偏好优化(RLHF/DPO)和强化学习训练(PPO/GRPO)等核心技术。</p>
<div class="article-grid">
<a href="ai-posttraining-overview.html" class="article-card-link">
<div class="article-card">
<h3>Post-Training 全景指南</h3>
<p>从 RLHF 到 GRPO 再到 Agentic RL,系统梳理后训练的核心方法和最新进展。</p>
</div>
</a>
<a href="ai-posttraining-peft.html" class="article-card-link">
<div class="article-card">
<h3>PEFT 详解</h3>
<p>参数高效微调方法详解,包括 LoRA、QLoRA、Prefix Tuning、Prompt Tuning 等技术。</p>
</div>
</a>
</div>
<a href="index.html#ai" class="back-link">← 返回 AI</a>
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<p>更新时间:2026-04-13</p>
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