<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>ChatGPT on Chen Kai Blog</title><link>https://www.chenk.top/en/tags/chatgpt/</link><description>Recent content in ChatGPT on Chen Kai Blog</description><generator>Hugo</generator><language>en</language><lastBuildDate>Thu, 25 Sep 2025 09:00:00 +0000</lastBuildDate><atom:link href="https://www.chenk.top/en/tags/chatgpt/index.xml" rel="self" type="application/rss+xml"/><item><title>Reinforcement Learning (12): RLHF and LLM Applications</title><link>https://www.chenk.top/en/reinforcement-learning/12-rlhf-and-llm-applications/</link><pubDate>Thu, 25 Sep 2025 09:00:00 +0000</pubDate><guid>https://www.chenk.top/en/reinforcement-learning/12-rlhf-and-llm-applications/</guid><description>&lt;p>GPT-3 (June 2020) and ChatGPT (November 2022) share most of their weights. The base model could write fluent prose, complete code, and continue any pattern you gave it. Yet, when asked a simple question, it might ramble, refuse for the wrong reasons, hallucinate citations, or produce toxic content. The two and a half years between GPT-3 and ChatGPT weren&amp;rsquo;t spent on larger transformers. Instead, they focused on &lt;strong>how to make the model useful&lt;/strong> — a reinforcement-learning problem.&lt;/p></description></item></channel></rss>