<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Chuhan Wang | Xunyi's Academic Website</title><link>https://jiangxunyi.github.io/authors/chuhan-wang/</link><atom:link href="https://jiangxunyi.github.io/authors/chuhan-wang/index.xml" rel="self" type="application/rss+xml"/><description>Chuhan Wang</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Wed, 08 Apr 2026 00:00:00 +0000</lastBuildDate><image><url>https://jiangxunyi.github.io/media/icon_hu12864870738162112581.png</url><title>Chuhan Wang</title><link>https://jiangxunyi.github.io/authors/chuhan-wang/</link></image><item><title>Generate, Filter, Control, Replay: A Comprehensive Survey of Rollout Strategies for LLM Reinforcement Learning</title><link>https://jiangxunyi.github.io/publication/rolloutsurvey/</link><pubDate>Wed, 08 Apr 2026 00:00:00 +0000</pubDate><guid>https://jiangxunyi.github.io/publication/rolloutsurvey/</guid><description>&lt;p>This survey organizes rollout design for language-model reinforcement learning into generation, filtering, control, and replay. It connects design choices to reliability, coverage, and computational cost, with examples across reasoning and agent tasks.&lt;/p></description></item></channel></rss>