<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2404.05892/</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/2404.05892/feed.xml" rel="self" type="application/rss+xml" /><item><title>Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence — mentioned by cs702</title><link>https://news.ycombinator.com/item?id=46879585</link><guid isPermaLink="false">urn:hackerpapers:mention:2404.05892:46879585</guid><pubDate>Wed, 04 Feb 2026 00:28:22 GMT</pubDate><description>&lt;p&gt;&amp;gt; Who out there is working on ... infinite history? Many people are still working on improving RNNs, mostly in academia. Examples off the top of my head: * RWKV: https://arxiv.org/abs/2006.16236 / https://arxiv.org/abs/2404.05892 https://arxiv.org/abs/2305.13048 * Linear attention: https://arxiv.org/abs/2503.14456 * State space models: https://arxiv.org/abs/2312.00752 / https://arxiv.org/abs/2405.21060 * Linear RNNs: https://arxiv.org/abs/2410.01201 Industry OTOH has gone all-in on Transformers.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2404.05892/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item><item><title>Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence — mentioned by jasonjmcghee</title><link>https://news.ycombinator.com/item?id=40299225</link><guid isPermaLink="false">urn:hackerpapers:mention:2404.05892:40299225</guid><pubDate>Wed, 08 May 2024 15:30:06 GMT</pubDate><description>&lt;p&gt;They reference &amp;quot;a GPT-3 model with 356M parameters&amp;quot; So GPT-3 Medium (from the GPT-3 paper) - feels pretty disingenuous to list that as no one is referencing that model when they say &amp;quot;GPT-3&amp;quot;, but the 175B model. I wasn&amp;#x27;t aware that size of the model (356M) was released- what am I missing here? I also think it&amp;#x27;s relatively well understood that (with our current methods) transformers have a tipping point with parameter count, and I don&amp;#x27;t know of any models less than ~3B that are useful- arguably 7B. Compare these benchmarks to, say, the RWKV 5/6 paper https://arxiv.org/abs/2404.05892&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2404.05892/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item><item><title>Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence — HN submission</title><link>https://news.ycombinator.com/item?id=39989270</link><guid isPermaLink="false">urn:hackerpapers:submission:2404.05892:39989270</guid><pubDate>Wed, 10 Apr 2024 10:56:11 GMT</pubDate><description>&lt;p&gt;Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;1 point; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2404.05892/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item><item><title>Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence — HN submission</title><link>https://news.ycombinator.com/item?id=39986366</link><guid isPermaLink="false">urn:hackerpapers:submission:2404.05892:39986366</guid><pubDate>Wed, 10 Apr 2024 02:23:50 GMT</pubDate><description>&lt;p&gt;Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;1 point; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2404.05892/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item></channel></rss>
