<?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/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>Thu, 08 Oct 2026 00:00:00 +0800</lastBuildDate><atom:link href="https://www.eigenform.ai/continuous-learning-tool-tips/categories/index.xml" rel="self" type="application/rss+xml"/><item><title>Why Continuous Learning</title><link>https://www.eigenform.ai/continuous-learning-tool-tips/categories/why-continuous-learning/</link><pubDate>Thu, 08 Oct 2026 00:00:00 +0800</pubDate><guid>https://www.eigenform.ai/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.&lt;/p&gt;
&lt;h2 id="what-this-series-covers"&gt;What This Series Covers&lt;/h2&gt;
&lt;p&gt;Each post opens with a question practitioners run into, explains the mechanism behind it, and compares the options, including reinforcement learning (RL) and RL with verifiable rewards (RLVR) where they apply. Only then does it introduce the approach Eigenform uses: fine-tuning small LoRA adapters on an agent&amp;rsquo;s own successful attempts, with safeguards against forgetting.&lt;/p&gt;</description></item></channel></rss>