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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Scaling Laws for Transfer · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2102.01293/</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/2102.01293/feed.xml" rel="self" type="application/rss+xml" /><item><title>Scaling Laws for Transfer — mentioned by gdiamos</title><link>https://news.ycombinator.com/item?id=48742554</link><guid isPermaLink="false">urn:hackerpapers:mention:2102.01293:48742554</guid><pubDate>Wed, 01 Jul 2026 05:18:58 GMT</pubDate><description>&lt;p&gt;Scaling laws assume the error metric and data distribution. There is a lot of follow on work that explains what happens as you change them, e.g. Scaling Laws for Transfer - https://arxiv.org/pdf/2102.01293 I think it’s fortunate that transfer works in a similar way. Common crawl (and Reddit, stack overflow, etc but not 4chan) was much easier to get access to at the time than using mechanical Turk. There is certainly room for more work. There were many papers on scaling laws in NeurIPS this year.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2102.01293/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>Scaling Laws for Transfer — HN submission</title><link>https://news.ycombinator.com/item?id=26011080</link><guid isPermaLink="false">urn:hackerpapers:submission:2102.01293:26011080</guid><pubDate>Wed, 03 Feb 2021 06:13:13 GMT</pubDate><description>&lt;p&gt;OpenAI – Scaling Laws for Transfer&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;3 points; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2102.01293/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
