This article kicks off a series on LLM post-training algorithms, focusing on PPO (Proximal Policy Optimization). The author explains the algorithm's core principles, including policy gradient methods and clipping mechanisms, and how they apply to fine-tuning large language models. The series plans to cover PPO, GRPO, and popular variants such as DAPO, providing a comprehensive resource for practitioners. While the content is educational, it serves as a solid foundation for understanding the evolving landscape of RLHF and post-training techniques. For engineers and researchers, this is a valuable starting point to grasp the mechanics behind model alignment.
A structured overview of PPO for LLM post-training, covering key concepts and recent variants like GRPO and DAPO.