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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Nemotron Elastic: Towards Efficient Many-in-One Reasoning LLMs · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2511.16664/</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/2511.16664/feed.xml" rel="self" type="application/rss+xml" /><item><title>Nemotron Elastic: Towards Efficient Many-in-One Reasoning LLMs — mentioned by m_ke</title><link>https://news.ycombinator.com/item?id=46018170</link><guid isPermaLink="false">urn:hackerpapers:mention:2511.16664:46018170</guid><pubDate>Sat, 22 Nov 2025 20:55:04 GMT</pubDate><description>&lt;p&gt;Yes it would require completely new hardware and most likely ditching gradient descent for alternative optimization methods, though I&amp;#x27;m not convinced that we&amp;#x27;d need to turn to discrete optimization. Some recent works that people might find interesting: - Evolution Strategies at the Hyperscale - https://eshyperscale.github.io/ - Introducing Nested Learning: A new ML paradigm for continual learning - https://research.google/blog/introducing-nested-learning-a-n... - Less is More: Recursive Reasoning with Tiny Networks - https://arxiv.org/abs/2510.04871 - Nemotron Elastic: Towards Efficient…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2511.16664/"&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>
