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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Byte Pair Encoding is Suboptimal for Language Model Pretraining · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2004.03720/</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/2004.03720/feed.xml" rel="self" type="application/rss+xml" /><item><title>Byte Pair Encoding is Suboptimal for Language Model Pretraining — mentioned by kacperlukawski</title><link>https://news.ycombinator.com/item?id=40334142</link><guid isPermaLink="false">urn:hackerpapers:mention:2004.03720:40334142</guid><pubDate>Sun, 12 May 2024 13:06:12 GMT</pubDate><description>&lt;p&gt;Are there any specific reasons for using BPE, not Unigram, in LLMs? I&amp;#x27;ve been trying to understand the impact of the tokenization algorithm, and Unigram was reported to be a better alternative (e.g., Byte Pair Encoding is Suboptimal for Language Model Pretraining: https://arxiv.org/abs/2004.03720). I understand that the unigram training process should eliminate under-trained tokens if trained on the same data as the LLM itself.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2004.03720/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item></channel></rss>
