<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Junwei Quan | LEAF Laboratory</title><link>http://leaf.utias.utoronto.ca/author/junwei-quan/</link><atom:link href="http://leaf.utias.utoronto.ca/author/junwei-quan/index.xml" rel="self" type="application/rss+xml"/><description>Junwei Quan</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Wed, 07 Oct 2026 00:00:00 +0000</lastBuildDate><image><url>http://leaf.utias.utoronto.ca/media/logo_hue87611c63d2e2a82e78083b2dc52db80_63556_300x300_fit_lanczos_3.png</url><title>Junwei Quan</title><link>http://leaf.utias.utoronto.ca/author/junwei-quan/</link></image><item><title>World-Model Policy Arbiter for Goal-Conditioned Reinforcement Learning</title><link>http://leaf.utias.utoronto.ca/publication/quan-2026-wmpa/</link><pubDate>Wed, 07 Oct 2026 00:00:00 +0000</pubDate><guid>http://leaf.utias.utoronto.ca/publication/quan-2026-wmpa/</guid><description>&lt;h2 id="summary">Summary&lt;/h2>
&lt;p>WMPA selects among frozen goal-conditioned policies by simulating their futures in a learned world model and scoring them with a shared value function, without retraining the policies.&lt;/p></description></item></channel></rss>