<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>KL-Divergence on Warpspace Blog</title><link>https://blog.caveduck.io/tags/kl-divergence/</link><description>Recent content in KL-Divergence on Warpspace Blog</description><generator>Hugo -- 0.154.5</generator><language>en-US</language><lastBuildDate>Mon, 04 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://blog.caveduck.io/tags/kl-divergence/index.xml" rel="self" type="application/rss+xml"/><item><title>Efficient ML #4, Low-bit Quantization: Fighting Outliers</title><link>https://blog.caveduck.io/posts/low-bit-quantization/</link><pubDate>Mon, 04 May 2026 00:00:00 +0000</pubDate><guid>https://blog.caveduck.io/posts/low-bit-quantization/</guid><description>FP4 has only 15 representable values, so how do modern GPUs run inference in 4 bits? The culprit that breaks low-bit quantization is the outlier. From an experiment where a single outlier erases an entire layer, to four cures, group quantization (MXFP4), KL clipping, AdaRound, and Hadamard rotation, we verify each in code, including an experiment where 2-bit accuracy comes back from 53% to 97%.</description></item></channel></rss>