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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Scalable Optimization in the Modular Norm · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2405.14813/</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/2405.14813/feed.xml" rel="self" type="application/rss+xml" /><item><title>Scalable Optimization in the Modular Norm — mentioned by phoenicyan</title><link>https://news.ycombinator.com/item?id=45390990</link><guid isPermaLink="false">urn:hackerpapers:mention:2405.14813:45390990</guid><pubDate>Fri, 26 Sep 2025 21:02:41 GMT</pubDate><description>&lt;p&gt;Well-done post, I&amp;#x27;d like to read more of their work and it&amp;#x27;s exciting to see these new ideas. Though as other people have said, the one set of empirical results that they present is a bit... confusing? I&amp;#x27;d think they&amp;#x27;d have some more compelling examples to present given all the pretty math. Their modular norm paper (https://arxiv.org/abs/2405.14813) has several more examples; see their appendix D in particular, but these are also mystifying. Yes they&amp;#x27;re interested in how things scale but am I the only one to whom it seems that the training losses they report are just not competitive with…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2405.14813/"&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>
