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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Reinforcement Learning of Theorem Proving · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1805.07563/</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/1805.07563/feed.xml" rel="self" type="application/rss+xml" /><item><title>Reinforcement Learning of Theorem Proving — mentioned by aSanchezStern</title><link>https://news.ycombinator.com/item?id=38042406</link><guid isPermaLink="false">urn:hackerpapers:mention:1805.07563:38042406</guid><pubDate>Fri, 27 Oct 2023 18:52:58 GMT</pubDate><description>&lt;p&gt;Sounds like you&amp;#x27;ve stumbled into the wonderful world of machine-learning guided proof synthesis! While I don&amp;#x27;t think the full system you&amp;#x27;re describing has been built yet, many similar systems and pieces have. In terms of the initial phase of supervised learning on existing proofs to prove new ones, there&amp;#x27;s TacticToe (https://arxiv.org/abs/1804.00596), Tactician (https://arxiv.org/pdf/2008.00120.pdf), CoqGym/ASTactic (https://arxiv.org/abs/1905.09381), Proverbot9001 (https://arxiv.org/abs/1907.07794), and Diva (https://dl.acm.org/doi/10.1145/3510003.3510138#sec-terms), among others. Most of…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1805.07563/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item><item><title>Reinforcement Learning of Theorem Proving — mentioned by xiphias2</title><link>https://news.ycombinator.com/item?id=19139662</link><guid isPermaLink="false">urn:hackerpapers:mention:1805.07563:19139662</guid><pubDate>Mon, 11 Feb 2019 23:43:53 GMT</pubDate><description>&lt;p&gt;Reinforcement learning can be used to improve theorem proving, but it probably needs more research: https://arxiv.org/abs/1805.07563&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1805.07563/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item><item><title>Reinforcement Learning of Theorem Proving — mentioned by AlexCoventry</title><link>https://news.ycombinator.com/item?id=17184766</link><guid isPermaLink="false">urn:hackerpapers:mention:1805.07563:17184766</guid><pubDate>Wed, 30 May 2018 03:12:25 GMT</pubDate><description>&lt;p&gt;I think a lot of GOFAI approaches ought to be revisited to see whether they benefit from the new perceptual and decision capabilities of Deep Learning systems. Alex Graves&amp;#x27;s papers are particularly good at this. Things like this reinforcement learner for theorem proving are pretty exciting possibilities. https://arxiv.org/pdf/1805.07563v1.pdf&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1805.07563/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item></channel></rss>
