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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Generation of Games for Opponent Model Differentiation · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2311.16781/</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/2311.16781/feed.xml" rel="self" type="application/rss+xml" /><item><title>Generation of Games for Opponent Model Differentiation — mentioned by michalsustr</title><link>https://news.ycombinator.com/item?id=45731030</link><guid isPermaLink="false">urn:hackerpapers:mention:2311.16781:45731030</guid><pubDate>Tue, 28 Oct 2025 10:14:32 GMT</pubDate><description>&lt;p&gt;The tournament measures the cumulative winnings. However, those can be far from the statistical expectation due to the variance of card distribution in poker. To establish a real winner, you need to play many games: &amp;gt; As seen in the Claudico match (20), even 80,000 games may not be enough to statistically significantly separate players whose skill differs by a considerable margin [1] It is possible to reduce the number of required games thanks to variance reduction techniques [1], but I don&amp;#x27;t think this is what the website does. To answer the question - &amp;quot;which &amp;#x27;quality&amp;#x27; of the LLMs this…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2311.16781/"&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>
