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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>One Epoch Is All You Need · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1906.06669/</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/1906.06669/feed.xml" rel="self" type="application/rss+xml" /><item><title>One Epoch Is All You Need — mentioned by kouteiheika</title><link>https://news.ycombinator.com/item?id=44254068</link><guid isPermaLink="false">urn:hackerpapers:mention:1906.06669:44254068</guid><pubDate>Thu, 12 Jun 2025 03:32:50 GMT</pubDate><description>&lt;p&gt;In general this is of course an active area of research, but yes, you can do something that and people have done it successfully[1] by adding extra layers to an existing model and then continuing to train it. You have to be careful about the &amp;quot;same data&amp;quot; part though; ideally you want to train once on unique data[2] as excessive duplication can harm the performance of the model[3], although if you have limited data a couple of training epochs might be safe and actually improve the performance of the model[4]. [1] -- https://arxiv.org/abs/2312.15166 [2] -- https://arxiv.org/abs/1906.06669 [3]…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1906.06669/"&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>
