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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Training Diffusion Models with Reinforcement Learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2305.13301/</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/2305.13301/feed.xml" rel="self" type="application/rss+xml" /><item><title>Training Diffusion Models with Reinforcement Learning — mentioned by kikijiji</title><link>https://news.ycombinator.com/item?id=37085385</link><guid isPermaLink="false">urn:hackerpapers:mention:2305.13301:37085385</guid><pubDate>Fri, 11 Aug 2023 05:25:23 GMT</pubDate><description>&lt;p&gt;Congrats on the launch. It&amp;#x27;s an interesting idea. What are your thoughts on DPPO[1] and if your model(s) could be integrated into that or a similar process to fine tune the original model? Could that potentially remove the need for this product? [1] https://arxiv.org/abs/2305.13301&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2305.13301/"&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>
