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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>bert2BERT: Towards Reusable Pretrained Language Models · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2110.07143/</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/2110.07143/feed.xml" rel="self" type="application/rss+xml" /><item><title>bert2BERT: Towards Reusable Pretrained Language Models — mentioned by calebkaiser</title><link>https://news.ycombinator.com/item?id=49987317</link><guid isPermaLink="false">urn:hackerpapers:mention:2110.07143:49987317</guid><pubDate>Wed, 07 Oct 2026 02:30:23 GMT</pubDate><description>&lt;p&gt;This is a productive line of research with lots to explore, if you&amp;#x27;re interested. There are different flavors based on what you&amp;#x27;re describing that you might like. Maybe you already know all of this, but just in case any other reader is curious :) A classic paper on &amp;quot;expanding&amp;quot; a neural network (the second part of your distil-then-grow loop) is Net2Net. Their goal is basically to get a smaller model&amp;#x27;s knowledge transferred into a larger one to bootstrap the larger model training: https://arxiv.org/abs/1511.05641 . Bert2Bert is basically the same idea but language models…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2110.07143/"&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>
