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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Optimal Linear Decay Learning Rate Schedules and Further Refinements · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2310.07831/</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/2310.07831/feed.xml" rel="self" type="application/rss+xml" /><item><title>Optimal Linear Decay Learning Rate Schedules and Further Refinements — mentioned by aarondefazio</title><link>https://news.ycombinator.com/item?id=39957818</link><guid isPermaLink="false">urn:hackerpapers:mention:2310.07831:39957818</guid><pubDate>Sun, 07 Apr 2024 03:04:31 GMT</pubDate><description>&lt;p&gt;The behavior is actually more complex than a 1/t schedule. It behaves like a linear decay schedule 1-t/T with fixed stopping time T, as if T had been chosen in advance as the current timestep. When warmup is included, this is similar to high performance triangular learning rate schedules. Schedules of the form 1/t schedules perform really poorly in practice, we actually did a large scale comparison that included them in a prior paper: https://arxiv.org/pdf/2310.07831.pdf&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2310.07831/"&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>
