<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Model Spec Midtraining: Improving How Alignment Training Generalizes · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2605.02087/</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/2605.02087/feed.xml" rel="self" type="application/rss+xml" /><item><title>Model Spec Midtraining: Improving How Alignment Training Generalizes — mentioned by zozbot234</title><link>https://news.ycombinator.com/item?id=48070453</link><guid isPermaLink="false">urn:hackerpapers:mention:2605.02087:48070453</guid><pubDate>Sat, 09 May 2026 00:25:40 GMT</pubDate><description>&lt;p&gt;Note that this result actually turns out to generalize well beyond Claude itself: Anthropic has actually conducted very similar research on open weight models, which they call Model Spec Midtraining https://arxiv.org/abs/2605.02087 (discussed at https://alignment.anthropic.com/2026/msm ) and they have released fine tuned versions of open models trained for a variety of toy &amp;quot;values&amp;quot; (Llama 3.1 8B, Qwen 2.5 32B, Qwen 3 32B) in order to show how the elicitation of these values in any one training context shapes the model&amp;#x27;s response to tangentially related questions…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2605.02087/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item></channel></rss>
