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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Diffusion Model Predictive Control · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2410.05364/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2410.05364/feed.xml" rel="self" type="application/rss+xml" /><item><title>Diffusion Model Predictive Control — mentioned by ssivark</title><link>https://news.ycombinator.com/item?id=48884042</link><guid isPermaLink="false">urn:hackerpapers:mention:2410.05364:48884042</guid><pubDate>Sun, 12 Jul 2026 19:48:43 GMT</pubDate><description>&lt;p&gt;Ha, interesting. I wasn&amp;#x27;t aware of Sutton&amp;#x27;s blog post, but if I might make a shameless plug, we demonstrated [1] exactly this problem (see section 4.4.3), and how multi-step world models (using diffusion models as the substrate) could be one potential answer. Since then, I have come to like temporally-abstract models more and more. Rolling out in time -- either step-by-step or many steps at once -- suffers from the tyranny of the specific. For long horizon planning with agents, I care (often only approximately) about where I can end up, and seldom about exactly when I end up there. Successor…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2410.05364/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
