Developer builds PPO algorithm from scratch, the RL method behind ChatGPT training

A developer documenting their reinforcement learning journey publicly implemented Proximal Policy Optimization (PPO) from scratch on Day 8 of their series. PPO improves on the Actor-Critic method by clipping policy update ratios to a 20% range per step, preventing training instability caused by large weight shifts. The algorithm also adds multiple training epochs per data batch and an entropy bonus to encourage longer exploration before the policy converges. After running the implementation on a gridworld environment, the critic independently mapped safe and dangerous states without being explicitly told their values. PPO has become an industry default due to its stability, simplicity, and versatility — including its role in fine-tuning large language models like ChatGPT via Reinforcement Learning from Human Feedback.