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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2408.08152/</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/2408.08152/feed.xml" rel="self" type="application/rss+xml" /><item><title>DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search — mentioned by dvrp</title><link>https://news.ycombinator.com/item?id=43842902</link><guid isPermaLink="false">urn:hackerpapers:mention:2408.08152:43842902</guid><pubDate>Wed, 30 Apr 2025 09:27:28 GMT</pubDate><description>&lt;p&gt;seems like it&amp;#x27;s not a new 671 B behemoth, just the V3 MoE engine with long-context RoPE + FA2, retargeted at theorem proving. see [1,2]: [1]: https://arxiv.org/abs/2405.14333 [2]: https://arxiv.org/abs/2408.08152&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2408.08152/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item><item><title>DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search — mentioned by maxrmk</title><link>https://news.ycombinator.com/item?id=41842480</link><guid isPermaLink="false">urn:hackerpapers:mention:2408.08152:41842480</guid><pubDate>Mon, 14 Oct 2024 21:59:45 GMT</pubDate><description>&lt;p&gt;There&amp;#x27;s a newer version of this model that takes a really cool RL based approach: https://arxiv.org/pdf/2408.08152&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2408.08152/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item><item><title>DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search — mentioned by jumploops</title><link>https://news.ycombinator.com/item?id=41813193</link><guid isPermaLink="false">urn:hackerpapers:mention:2408.08152:41813193</guid><pubDate>Fri, 11 Oct 2024 20:10:00 GMT</pubDate><description>&lt;p&gt;&amp;gt; There is just no way you can build reliable agents on this foundation, where changing a word or two in irrelevant ways or adding a few bits of irrelevant info can give you a different answer. LLMs are not magic bullets for every problem, but that doesn&amp;#x27;t preclude them from being used to build reliable systems or &amp;quot;agents.&amp;quot; It&amp;#x27;s clear that we don&amp;#x27;t yet have the all-encompassing AGI architecture, especially with the transformer model alone, but adding steps beyond the transformer leads to interesting results, as we&amp;#x27;ve seen with current coding tools and the new o1-series models by OpenAI. For…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2408.08152/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item><item><title>DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search — HN submission</title><link>https://news.ycombinator.com/item?id=41272063</link><guid isPermaLink="false">urn:hackerpapers:submission:2408.08152:41272063</guid><pubDate>Sat, 17 Aug 2024 03:34:57 GMT</pubDate><description>&lt;p&gt;DeepSeek-Prover-v1.5: Proof Assistant for RL and Monte-Carlo Tree Search&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;1 point; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2408.08152/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item></channel></rss>
