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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Teaching Large Language Models to Reason with Reinforcement Learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2403.04642/</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/2403.04642/feed.xml" rel="self" type="application/rss+xml" /><item><title>Teaching Large Language Models to Reason with Reinforcement Learning — mentioned by fabmilo</title><link>https://news.ycombinator.com/item?id=39663486</link><guid isPermaLink="false">urn:hackerpapers:mention:2403.04642:39663486</guid><pubDate>Sun, 10 Mar 2024 23:58:34 GMT</pubDate><description>&lt;p&gt;I believe this kind of graph exploration is what we need to progress reasoning in AI. Plain LLMS will fail. The link has tons of good references, including the Zobrist hashing https://en.wikipedia.org/wiki/Zobrist_hashing for game tables. We need to find a good hashing for language based state description so that graph exploration doesn&amp;#x27;t explode computationally. Another good read for Tree Search is Thinking Fast and Slow: https://arxiv.org/abs/1705.08439 and Teaching Large Language Models to Reason with Reinforcement Learning: https://arxiv.org/abs/2403.04642 comparing the MCTS approach to…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2403.04642/"&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>
