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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Kinetics: Rethinking Test-Time Scaling Laws · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2506.05333/</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/2506.05333/feed.xml" rel="self" type="application/rss+xml" /><item><title>Kinetics: Rethinking Test-Time Scaling Laws — mentioned by ZMC2019</title><link>https://news.ycombinator.com/item?id=44203684</link><guid isPermaLink="false">urn:hackerpapers:mention:2506.05333:44203684</guid><pubDate>Fri, 06 Jun 2025 18:33:22 GMT</pubDate><description>&lt;p&gt;Hi everyone! We&amp;#x27;re excited to share our new work on rethinking test-time scaling laws. Arxiv link: https://arxiv.org/abs/2506.05333 Website: https://infini-ai-lab.github.io/Kinetics/ Twitter: https://x.com/InfiniAILab/status/1931053042876768586 Summary: (1) Kinetics Scaling Law: We identify a shift in the scaling law for SOTA reasoning models, diverging from existing compute-optimal laws that overlook critical memory access bottlenecks. Our eFLOPs-based holistic analysis reveals that small model effectiveness is often overestimated, suggesting that we should prioritize increasing model size…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2506.05333/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
