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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>SKATE, a Scalable Tournament Eval: Weaker LLMs differentiate between stronger ones using verifiable challenges · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2508.06111/</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/2508.06111/feed.xml" rel="self" type="application/rss+xml" /><item><title>SKATE, a Scalable Tournament Eval: Weaker LLMs differentiate between stronger ones using verifiable challenges — mentioned by crs_gentleman</title><link>https://news.ycombinator.com/item?id=48774981</link><guid isPermaLink="false">urn:hackerpapers:mention:2508.06111:48774981</guid><pubDate>Fri, 03 Jul 2026 13:46:24 GMT</pubDate><description>&lt;p&gt;We tried this, and it works :) https://arxiv.org/abs/2508.06111 You have to be careful about degenerate / duplicate Qs, as a sibling commenter mentioned. Recently though, we found that reasoning models have trouble making code-output-prediction tasks (the initial family of verifiable tasks we started with) which other reasoning models can&amp;#x27;t solve. We started looking into harder / more agentic tasks (e.g. passing tests, using AISI&amp;#x27;s Inspect framework) but deprioritised.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2508.06111/"&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>
