<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Post-Training on Chen Kai Blog</title><link>https://www.chenk.top/en/tags/post-training/</link><description>Recent content in Post-Training on Chen Kai Blog</description><generator>Hugo</generator><language>en</language><lastBuildDate>Mon, 30 Mar 2026 09:00:00 +0000</lastBuildDate><atom:link href="https://www.chenk.top/en/tags/post-training/index.xml" rel="self" type="application/rss+xml"/><item><title>LLM Engineering (4): Post-training — SFT, DPO, RLHF, RLAIF</title><link>https://www.chenk.top/en/llm-engineering/04-post-training/</link><pubDate>Mon, 30 Mar 2026 09:00:00 +0000</pubDate><guid>https://www.chenk.top/en/llm-engineering/04-post-training/</guid><description>&lt;p>A base model from pretraining can complete text but cannot follow instructions, refuse harmful requests, or maintain a persona—these are post-training behaviors. Post-training is where the gap between a research paper&amp;rsquo;s claims and a production-grade model lies. This chapter covers what each post-training algorithm optimizes, why most reward models are subtly flawed, and the effective methods for 2026.&lt;/p>
&lt;p>&lt;figure class="article-figure">
 &lt;img src="https://blog-pic-ck.oss-cn-beijing.aliyuncs.com/posts/en/llm-engineering/04-post-training/illustration_1.png" alt="LLM Engineering (4): Post-training — SFT, DPO, RLHF, RLAIF — Chapter overview" loading="lazy" decoding="async" class="content-image">
 
&lt;/figure>
&lt;/p></description></item></channel></rss>