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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Stabilizing Transformers for Reinforcement Learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1910.06764/</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/1910.06764/feed.xml" rel="self" type="application/rss+xml" /><item><title>Stabilizing Transformers for Reinforcement Learning — mentioned by ctoth</title><link>https://news.ycombinator.com/item?id=22293186</link><guid isPermaLink="false">urn:hackerpapers:mention:1910.06764:22293186</guid><pubDate>Mon, 10 Feb 2020 20:54:28 GMT</pubDate><description>&lt;p&gt;There&amp;#x27;s interesting research out of Deepmind on two parts of this, which are using the Transformer model in Reinforcement Learning contexts[0] and creating textual GANs[1]. As you are probably aware, GANs are one of the important tools that have driven forward image synthesis and until recently it was impossible to apply them to text, so I expect this to push us quite a bit forward. There&amp;#x27;s also ongoing work in the selection of the metric to use to evaluate the generated text, and discriminate between human and machine-generated text. [0] https://arxiv.org/abs/1910.06764 [1]…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1910.06764/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>Stabilizing Transformers for Reinforcement Learning — mentioned by lucidrains</title><link>https://news.ycombinator.com/item?id=21650769</link><guid isPermaLink="false">urn:hackerpapers:mention:1910.06764:21650769</guid><pubDate>Wed, 27 Nov 2019 18:53:35 GMT</pubDate><description>&lt;p&gt;Another work in the opposite direction, introducing gating in Transformer-xl https://arxiv.org/abs/1910.06764&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1910.06764/"&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>
