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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Reinforcement Learning on Pre-Training Data · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2509.19249/</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/2509.19249/feed.xml" rel="self" type="application/rss+xml" /><item><title>Reinforcement Learning on Pre-Training Data — mentioned by andy12_</title><link>https://news.ycombinator.com/item?id=47037221</link><guid isPermaLink="false">urn:hackerpapers:mention:2509.19249:47037221</guid><pubDate>Mon, 16 Feb 2026 16:47:48 GMT</pubDate><description>&lt;p&gt;Reinforcement learning is not done with random data found on the internet; it&amp;#x27;s done with curated high-quality labeled datasets. Although there have been approaches that try to apply reinforcement learning to pre-training[1] (to learn in an unsupervised way a predict-the-next-sentence objective), as far as I know it doesn&amp;#x27;t scale. [1] https://arxiv.org/pdf/2509.19249&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2509.19249/"&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>
