<?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>Scaling-Laws on The Standard Error</title><link>https://jongha-jeon-dev.github.io/standarderror/tags/scaling-laws/</link><description>Recent content in Scaling-Laws on The Standard Error</description><generator>Hugo -- 0.123.7</generator><language>en-gb</language><lastBuildDate>Tue, 13 Oct 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://jongha-jeon-dev.github.io/standarderror/tags/scaling-laws/index.xml" rel="self" type="application/rss+xml"/><item><title>ML 4: A Learning Curve Fitted Early Promises More Than More Data Delivers</title><link>https://jongha-jeon-dev.github.io/standarderror/lectures/ml-4-learning-curves/</link><pubDate>Thu, 08 Oct 2026 00:00:00 +0000</pubDate><guid>https://jongha-jeon-dev.github.io/standarderror/lectures/ml-4-learning-curves/</guid><description>Fit a power law to a pilot study&amp;#39;s errors and it tells you what more data will buy. Checked against what more data did buy, it was optimistic every time, sometimes past the Bayes error.</description></item><item><title>ML 30: A Power Law Fitted on Small Models Cannot See the Floor</title><link>https://jongha-jeon-dev.github.io/standarderror/lectures/ml-30-scaling/</link><pubDate>Tue, 13 Oct 2026 00:00:00 +0000</pubDate><guid>https://jongha-jeon-dev.github.io/standarderror/lectures/ml-30-scaling/</guid><description>Eight character transformers, one budget of data and steps. The law fitted to the five smallest predicts the next size almost exactly and the largest too optimistically.</description></item></channel></rss>