OpenAI adopts Proximal Policy Optimization as default reinforcement learning algorithm

AI Impact Summary

OpenAI is introducing Proximal Policy Optimization (PPO) as a new RL algorithm class and designating it as the default due to its performance and ease of use. This shifts training defaults and will affect how experiments are configured, requiring teams to re-tune hyperparameters such as clip range, learning rate, and entropy weight to match PPO behavior. Production teams should revalidate policy performance and stability, as models trained with prior algorithms may not transfer directly and faster iteration could change cost and deployment timelines.