<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Diffusion-LM Improves Controllable Text Generation · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2205.14217/</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/2205.14217/feed.xml" rel="self" type="application/rss+xml" /><item><title>Diffusion-LM Improves Controllable Text Generation — mentioned by jxmorris12</title><link>https://news.ycombinator.com/item?id=43131724</link><guid isPermaLink="false">urn:hackerpapers:mention:2205.14217:43131724</guid><pubDate>Fri, 21 Feb 2025 19:20:45 GMT</pubDate><description>&lt;p&gt;Thanks for bringing this up! As far as I understand it current text diffusion models are limited to fairly short context windows. The idea of a text diffusion model continuously updating and revising a million-token-long chain-of-thought is pretty mind-boggling. I agree that these non-autoregressive models could potentially behave in completely different ways. That said, I&amp;#x27;m pretty sure we&amp;#x27;re a long way from building equally-competent diffusion-based base models, let alone reasoning models. If anyone&amp;#x27;s interested in this topic, here are some more foundational papers to take a look at…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2205.14217/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item></channel></rss>
