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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Overcoming the Stability Gap in Continual Learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2306.01904/</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/2306.01904/feed.xml" rel="self" type="application/rss+xml" /><item><title>Overcoming the Stability Gap in Continual Learning — mentioned by chriskanan</title><link>https://news.ycombinator.com/item?id=40392509</link><guid isPermaLink="false">urn:hackerpapers:mention:2306.01904:40392509</guid><pubDate>Fri, 17 May 2024 17:54:38 GMT</pubDate><description>&lt;p&gt;This study is great and addresses a question I&amp;#x27;ve had about LoRA for a while. In a continual learning paper from last year, I found LoRA was extremely effective for faster fine-tuning and not forgetting the original dataset: https://arxiv.org/abs/2306.01904&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2306.01904/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CV</category></item></channel></rss>
