<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Categories on Eigenform Continuous Learning Tool Tips</title><link>https://www.eigenform.ai/recursive-self-improvement/continuous-learning-tool-tips/categories/</link><description>Recent content in Categories on Eigenform Continuous Learning Tool Tips</description><generator>Hugo</generator><language>en-US</language><lastBuildDate>Mon, 12 Oct 2026 00:00:00 +0800</lastBuildDate><atom:link href="https://www.eigenform.ai/recursive-self-improvement/continuous-learning-tool-tips/categories/index.xml" rel="self" type="application/rss+xml"/><item><title>Why Continuous Learning</title><link>https://www.eigenform.ai/recursive-self-improvement/continuous-learning-tool-tips/categories/why-continuous-learning/</link><pubDate>Thu, 08 Oct 2026 00:00:00 +0800</pubDate><guid>https://www.eigenform.ai/recursive-self-improvement/continuous-learning-tool-tips/categories/why-continuous-learning/</guid><description>&lt;p&gt;Most AI agents never learn from their own work. The model is trained once and then frozen, and anything it seems to &amp;ldquo;remember&amp;rdquo; lives in prompts, retrieval and memory files. This series starts with the problems before the solutions: why an agent that solved a task yesterday is no better today, why you can&amp;rsquo;t simply keep fine-tuning, and where reinforcement learning stops helping. Our answer is modified SFT for policy-free learning, a new approach to SFT for continuous learning, and every post builds towards it.&lt;/p&gt;</description></item></channel></rss>