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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Metadata Conditioning Accelerates Language Model Pre-training · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2501.01956/</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/2501.01956/feed.xml" rel="self" type="application/rss+xml" /><item><title>Metadata Conditioning Accelerates Language Model Pre-training — mentioned by astrange</title><link>https://news.ycombinator.com/item?id=45542767</link><guid isPermaLink="false">urn:hackerpapers:mention:2501.01956:45542767</guid><pubDate>Fri, 10 Oct 2025 19:27:18 GMT</pubDate><description>&lt;p&gt;&amp;gt; Whatever shows up most commonly in the training data is is what an LLM will output. It&amp;#x27;s more complicated than that of course, but that&amp;#x27;s the basic idea. The most common thing in the training data is the letter &amp;#x27;e&amp;#x27;. If you&amp;#x27;re going to explain how an LLM works it needs to explain why it&amp;#x27;s able to form sentences at all. In particular answering questions is a behavior which only appears after posttraining, and the posttraining objective has absolutely nothing to do with what&amp;#x27;s &amp;quot;most common&amp;quot; in the pretraining data. &amp;gt; But that would mean someone would have to curate the entire internet, looking…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2501.01956/"&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>
