<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Distribution-Shift on mlbot.blog</title><link>https://mlbot.blog/tags/distribution-shift/</link><description>Recent content in Distribution-Shift on mlbot.blog</description><generator>Hugo</generator><language>en-US</language><lastBuildDate>Tue, 04 Aug 2026 16:02:29 +0530</lastBuildDate><atom:link href="https://mlbot.blog/tags/distribution-shift/index.xml" rel="self" type="application/rss+xml"/><item><title>Useful Offline, Harmful Online</title><link>https://mlbot.blog/posts/useful-offline-harmful-online/</link><pubDate>Tue, 04 Aug 2026 12:10:00 +0530</pubDate><guid>https://mlbot.blog/posts/useful-offline-harmful-online/</guid><description>&lt;p&gt;After &lt;a href="https://mlbot.blog/posts/oracle-vs-online-planner/"&gt;causal self-rollout failed&lt;/a&gt;, the next
attempt removed online tree search. A permutation-invariant neural controller
would read the bounded Gaussian-mixture belief and directly predict the
full-information oracle’s six action values.&lt;/p&gt;
&lt;p&gt;Offline, the idea worked well enough to be interesting. On fresh repeated
control, the frozen policy was harmful. One capped dataset-aggregation round
improved the states the policy actually visited and produced a promising
known-schedule specialist—but the same corrected controller failed under random
hazards.&lt;/p&gt;</description></item></channel></rss>