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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>It's All Training: A Fully Synthetic Single-Stage Recipe for LLMs · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2609.37891/</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/2609.37891/feed.xml" rel="self" type="application/rss+xml" /><item><title>It's All Training: A Fully Synthetic Single-Stage Recipe for LLMs — mentioned by mmargenot</title><link>https://news.ycombinator.com/item?id=50091255</link><guid isPermaLink="false">urn:hackerpapers:mention:2609.37891:50091255</guid><pubDate>Wed, 14 Oct 2026 17:19:17 GMT</pubDate><description>&lt;p&gt;LLMs can be pre-trained on LLM outputs, and there have been a few recent interesting implementations of this. - https://arxiv.org/abs/2609.37891 - https://arxiv.org/pdf/2508.10975 - https://www.datologyai.com/blog/eating-the-rainbow-a-primer-... As well as recent model releases that used large fractions of synthetic data (Arcee Trinity): - https://arxiv.org/pdf/2602.17004v1 It&amp;#x27;s mostly a matter of increasingly complex and sophisticated sampling methodologies to sift for the valuable synthetic tokens. There have also been some interesting results about models that use the same data converging…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2609.37891/"&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>
