<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>schristoph.online</title><link>https://schristoph.online/tags/modeltraining/</link><description>Personal homepage and blog of Stefan Christoph</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><copyright>Stefan Christoph. All rights reserved.</copyright><lastBuildDate>Mon, 27 Apr 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://schristoph.online/tags/modeltraining/index.xml" rel="self" type="application/rss+xml"/><item><title>Self-Improving Models: What MiniMax M2.7 Actually Does</title><link>https://schristoph.online/blog/self-improving-models/?utm=rss-feed</link><pubDate>Mon, 27 Apr 2026 00:00:00 +0000</pubDate><guid>https://schristoph.online/blog/self-improving-models/</guid><description>&lt;h2 id="the-headline-vs-the-reality">The Headline vs The Reality&lt;/h2>
&lt;p>&amp;ldquo;Model trains itself over 100+ autonomous cycles.&amp;rdquo; That was the headline when MiniMax released M2.7 on March 18, 2026 [1]. It sounds like science fiction: a model bootstrapping its own intelligence in a recursive loop.&lt;/p>
&lt;p>The reality is more subtle, more interesting, and more relevant to how we&amp;rsquo;ll build AI systems in the near future.&lt;/p>
&lt;h2 id="what-self-evolution-actually-means">What &amp;ldquo;Self-Evolution&amp;rdquo; Actually Means&lt;/h2>
&lt;p>M2.7 handled 30-50% of its own RL (reinforcement learning) workflow: data pipeline management, experiment tracking, log analysis, and automated code merging. It ran 100+ autonomous improvement cycles. That&amp;rsquo;s genuinely impressive.&lt;/p></description></item></channel></rss>