<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Autonomous Learning on SoloSoft</title><link>https://www.solosoft.dev/tags/autonomous-learning/</link><description>Recent content in Autonomous Learning on SoloSoft</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Fri, 01 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://www.solosoft.dev/tags/autonomous-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>AutoDidact: Self-Teaching Framework for LLM Improvement</title><link>https://www.solosoft.dev/post/autodidact-llm-2026/</link><pubDate>Fri, 01 May 2026 00:00:00 +0000</pubDate><guid>https://www.solosoft.dev/post/autodidact-llm-2026/</guid><description>&lt;p&gt;The most expensive part of improving AI models has always been data: collecting, cleaning, and annotating millions of examples requires enormous human effort. &lt;strong&gt;AutoDidact&lt;/strong&gt; explores a tantalizing alternative: what if language models could teach themselves? Created by researcher dCaples, this open-source framework implements iterative self-improvement loops where LLMs generate their own training data, evaluate their own outputs, and fine-tune themselves &amp;ndash; all without human intervention.&lt;/p&gt;
&lt;p&gt;The concept draws inspiration from a rich body of research on self-supervised learning, self-play in games (like AlphaGo), and more recent work on constitutional AI and self-rewarding language models. AutoDidact packages these ideas into a practical framework that researchers and practitioners can apply to their own models and tasks.&lt;/p&gt;</description></item></channel></rss>