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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Heavy-Tailed Class Imbalance and Why Adam Outperforms Gradient Descent on Language Models · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2402.19449/</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/2402.19449/feed.xml" rel="self" type="application/rss+xml" /><item><title>Heavy-Tailed Class Imbalance and Why Adam Outperforms Gradient Descent on Language Models — mentioned by neodypsis</title><link>https://news.ycombinator.com/item?id=40190917</link><guid isPermaLink="false">urn:hackerpapers:mention:2402.19449:40190917</guid><pubDate>Sun, 28 Apr 2024 19:02:19 GMT</pubDate><description>&lt;p&gt;&amp;gt; Seems like we still have a long way to go after Adam... A preprint in arxiv suggests that Adam works better than SGD for training LLMs due to the issue of class-imbalance [0]. It appears that scaling the gradient step helps with the training, for example, see another approach suggested in [1]. 0. https://arxiv.org/pdf/2402.19449 1. https://arxiv.org/pdf/2402.02347&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2402.19449/"&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>
