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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Sparse Matrix in Large Language Model Fine-tuning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2405.15525/</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/2405.15525/feed.xml" rel="self" type="application/rss+xml" /><item><title>Sparse Matrix in Large Language Model Fine-tuning — mentioned by WJW</title><link>https://news.ycombinator.com/item?id=45057127</link><guid isPermaLink="false">urn:hackerpapers:mention:2405.15525:45057127</guid><pubDate>Thu, 28 Aug 2025 21:15:48 GMT</pubDate><description>&lt;p&gt;Not always, or rather not exclusively. For example, some types of distillation benefit from sparse-ifying the dense-ish matrices the original was made of [1]. There&amp;#x27;s also a lot of benefit to be had from sparsity in finetuning [2]. LLMs were merely one of the examples though, don&amp;#x27;t focus too much on them. The point was that sparse matmul makes up the bulk of scientific computations and a huge amount of industrial computations too. It&amp;#x27;s probably second only to the FFT in importance, so it would be wild if radix sort managed to eclipse it somehow. [1]…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2405.15525/"&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>
