<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Optimization Techniques on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/optimization-techniques/</link><description>Recent content in Optimization Techniques on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sat, 17 Jan 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/optimization-techniques/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><item><title>Chapter 14: Model Training Workflows &amp;amp; Optimization Techniques</title><link>https://ai-blog.noorshomelab.dev/ai-ml-career-path-2026/training-workflows-optimization/</link><pubDate>Sat, 17 Jan 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/ai-ml-career-path-2026/training-workflows-optimization/</guid><description>&lt;h2 id="introduction-to-model-training-workflows--optimization"&gt;Introduction to Model Training Workflows &amp;amp; Optimization&lt;/h2&gt;
&lt;p&gt;Welcome back, future AI engineer! In the previous chapters, we laid the groundwork by understanding the mathematical foundations of AI, classic machine learning algorithms, and delving into the fascinating world of neural networks and their diverse architectures. You&amp;rsquo;ve learned how to construct these powerful models. But a model, no matter how well-designed, is useless until it learns from data. That&amp;rsquo;s where &lt;strong&gt;model training workflows&lt;/strong&gt; come in.&lt;/p&gt;</description></item></channel></rss>