<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Agent Training on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/agent-training/</link><description>Recent content in Agent Training on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Thu, 06 Nov 2025 22:00:00 +0530</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/agent-training/index.xml" rel="self" type="application/rss+xml"/><item><title>Advanced Optimization Algorithms</title><link>https://ai-blog.noorshomelab.dev/agentic-lightening-guide/advanced-optimization-algorithms/</link><pubDate>Thu, 06 Nov 2025 22:00:00 +0530</pubDate><guid>https://ai-blog.noorshomelab.dev/agentic-lightening-guide/advanced-optimization-algorithms/</guid><description>&lt;h2 id="advanced-optimization-algorithms"&gt;Advanced Optimization Algorithms&lt;/h2&gt;
&lt;p&gt;With a solid understanding of rollouts and rewards, we can now delve into the powerful optimization algorithms that Agentic Lightening integrates to make your AI agents truly adaptive and performant. Agentic Lightening is designed to be algorithm-agnostic, providing hooks for various techniques. While its initial strong focus is on Reinforcement Learning (RL), it also supports Automatic Prompt Optimization (APO) and can facilitate Supervised Fine-tuning (SFT).&lt;/p&gt;
&lt;p&gt;This chapter will provide an overview of these algorithms, explain their relevance in the context of agent training, and show how they conceptually fit into the Agentic Lightening framework.&lt;/p&gt;</description></item></channel></rss>