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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Scaling Laws for Reward Model Overoptimization · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2210.10760/</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/2210.10760/feed.xml" rel="self" type="application/rss+xml" /><item><title>Scaling Laws for Reward Model Overoptimization — mentioned by astrange</title><link>https://news.ycombinator.com/item?id=34424899</link><guid isPermaLink="false">urn:hackerpapers:mention:2210.10760:34424899</guid><pubDate>Wed, 18 Jan 2023 09:25:13 GMT</pubDate><description>&lt;p&gt;I meant the blog post. https://openai.com/blog/chatgpt/ &amp;gt; The model is often excessively verbose and overuses certain phrases, such as restating that it’s a language model trained by OpenAI. These issues arise from biases in the training data (trainers prefer longer answers that look more comprehensive) and well-known over-optimization issues.12 &amp;gt; Stiennon, Nisan, et al. “Learning to summarize with human feedback.” Advances in Neural Information Processing Systems 33 (2020): 3008-3021. ↩ &amp;gt; Gao, Leo, John Schulman, and Jacob Hilton. “Scaling Laws for Reward Model Overoptimization.” arXiv…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2210.10760/"&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>
