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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Multi-Game Decision Transformers · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2205.15241/</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/2205.15241/feed.xml" rel="self" type="application/rss+xml" /><item><title>Multi-Game Decision Transformers — mentioned by ninetyninenine</title><link>https://news.ycombinator.com/item?id=41928578</link><guid isPermaLink="false">urn:hackerpapers:mention:2205.15241:41928578</guid><pubDate>Wed, 23 Oct 2024 19:36:07 GMT</pubDate><description>&lt;p&gt;I have. The failure is with you and your extracting only the mechanical functionality of LLMs as all an LLM is. The high level macro structure of what ends up being trained is something nobody understands. Regardless of WHAT I say, the general consensus among academia and professionals is extremely different from what you characterize. I can cite dozens of research papers contrary to your point in a simple google search: https://www.sciencedirect.com/science/article/pii/S016028962... https://www.topbots.com/llm-reasoning-research-papers/ https://arxiv.org/abs/2305.10601…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2205.15241/"&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>
