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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Mastering Chess with a Transformer Model · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2409.12272/</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/2409.12272/feed.xml" rel="self" type="application/rss+xml" /><item><title>Mastering Chess with a Transformer Model — mentioned by mquander</title><link>https://news.ycombinator.com/item?id=47055827</link><guid isPermaLink="false">urn:hackerpapers:mention:2409.12272:47055827</guid><pubDate>Wed, 18 Feb 2026 01:23:38 GMT</pubDate><description>&lt;p&gt;I don&amp;#x27;t really understand the criticism. The authors aren&amp;#x27;t claiming to have the strongest chess engine without search. They are just showing that they got a chess engine to a respectable level with their process, which is somewhat different from LC0. They do in fact explain that explicitly: &amp;gt; Leela Chess Zero’s networks, which are trained with self-play and RL, achieve higher Elo ratings without using explicit search at test time than our transformers, which we trained via supervised learning. However, in contrast to our work, very strong chess performance (at low computational cost) is the…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2409.12272/"&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>
