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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>RouteLLM: Learning to Route LLMs with Preference Data · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2406.18665/</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/2406.18665/feed.xml" rel="self" type="application/rss+xml" /><item><title>RouteLLM: Learning to Route LLMs with Preference Data — mentioned by gardnr</title><link>https://news.ycombinator.com/item?id=42492368</link><guid isPermaLink="false">urn:hackerpapers:mention:2406.18665:42492368</guid><pubDate>Mon, 23 Dec 2024 06:51:53 GMT</pubDate><description>&lt;p&gt;In case you haven&amp;#x27;t seen this one yet: Title: &amp;quot;RouteLLM: Learning to Route LLMs with Preference Data&amp;quot; Abstract: Large language models (LLMs) exhibit impressive capabilities across a wide range of tasks, yet the choice of which model to use often involves a trade-off between performance and cost. More powerful models, though effective, come with higher expenses, while less capable models are more cost-effective. To address this dilemma, we propose several efficient router models that dynamically select between a stronger and a weaker LLM during inference, aiming to optimize the balance between…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2406.18665/"&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>
