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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>VeLO: Training Versatile Learned Optimizers by Scaling Up · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2211.09760/</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/2211.09760/feed.xml" rel="self" type="application/rss+xml" /><item><title>VeLO: Training Versatile Learned Optimizers by Scaling Up — mentioned by Legend2440</title><link>https://news.ycombinator.com/item?id=36779383</link><guid isPermaLink="false">urn:hackerpapers:mention:2211.09760:36779383</guid><pubDate>Tue, 18 Jul 2023 21:33:53 GMT</pubDate><description>&lt;p&gt;No one has the scale to make that happen. It&amp;#x27;s about information. Gradient-free methods integrate little or no information about the problem; they&amp;#x27;re a blind watchmaker. This works, but it&amp;#x27;s slow and gets slower the bigger your problem is. (curse of dimensionality) Gradients integrate some limited information about the problem. This lets you find solutions much faster, and neural networks are structured specifically to be easy to optimize with gradients. Local minima don&amp;#x27;t seem to be a problem. The future is probably even smarter optimizers that integrate more information about the problem…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2211.09760/"&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>
