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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Guiding Pretraining in Reinforcement Learning with Large Language Models · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2302.06692/</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/2302.06692/feed.xml" rel="self" type="application/rss+xml" /><item><title>Guiding Pretraining in Reinforcement Learning with Large Language Models — mentioned by sharemywin</title><link>https://news.ycombinator.com/item?id=35992541</link><guid isPermaLink="false">urn:hackerpapers:mention:2302.06692:35992541</guid><pubDate>Thu, 18 May 2023 19:00:59 GMT</pubDate><description>&lt;p&gt;I think with reinforcement learning and adversarial play you don&amp;#x27;t need it to understand for it to have unintended consequences and &amp;quot;break out of it&amp;#x27;s cage&amp;quot; Surprising behaviors We’ve shown that agents can learn sophisticated tool use in a high fidelity physics simulator; however, there were many lessons learned along the way to this result. Building environments is not easy and it is quite often the case that agents find a way to exploit the environment you build or the physics engine in an unintended way. Emergent tool use from multi-agent interaction…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2302.06692/"&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>
