https://ai-blog.noorshomelab.dev/tags/ppo/Recent content in PPO on AI VOIDHugoenFri, 30 Jan 2026 00:00:00 +0000https://ai-blog.noorshomelab.dev/tunix-mastery-2026/12-advanced-rlhf-ppo/Fri, 30 Jan 2026 00:00:00 +0000https://ai-blog.noorshomelab.dev/tunix-mastery-2026/12-advanced-rlhf-ppo/<h2 id="introduction">Introduction</h2> <p>Welcome to Chapter 12! So far, we&rsquo;ve explored the foundational elements of post-training Large Language Models (LLMs) with Tunix, including supervised fine-tuning and the basics of reward modeling. In this chapter, we&rsquo;re going to elevate our game by diving into more advanced strategies for Reinforcement Learning from Human Feedback (RLHF), with a particular focus on <strong>Proximal Policy Optimization (PPO)</strong>.</p> <p>PPO is a cornerstone algorithm in modern RLHF pipelines, enabling robust and efficient alignment of LLMs with human preferences. Understanding PPO is crucial for anyone looking to build highly effective and ethically aligned language models. We&rsquo;ll break down this powerful algorithm into digestible steps, explore its core mechanics, and demonstrate how Tunix empowers you to implement it for your LLM post-training tasks.</p>