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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Grokking modular arithmetic · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2301.02679/</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/2301.02679/feed.xml" rel="self" type="application/rss+xml" /><item><title>Grokking modular arithmetic — mentioned by p-e-w</title><link>https://news.ycombinator.com/item?id=46678386</link><guid isPermaLink="false">urn:hackerpapers:mention:2301.02679:46678386</guid><pubDate>Mon, 19 Jan 2026 12:47:45 GMT</pubDate><description>&lt;p&gt;&amp;gt; The Turkey was an LLM. It predicted the future based entirely on the distribution of the past. It had no &amp;quot;understanding&amp;quot; of the purpose of the farmer. But we already know that LLMs can do much better than that. See the famous “grokking” paper[1], which demonstrates that with sufficient training, a transformer can learn a deep generalization of its training data that isn’t just a probabilistic interpolation or extrapolation from previous inputs. Many of the supposed “fundamental limitations” of LLMs have already been disproven in research. And this is a standard transformer architecture; it…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2301.02679/"&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>
