John Schulman and colleagues at OpenAI released Proximal Policy Optimization (PPO), publishing both the paper and an implementation. PPO performed comparably to or better than existing methods while being much simpler to implement and tune, and had already become OpenAI's default algorithm at the time of release. It went on to become one of the most widely used reinforcement learning methods and later played a central role in fine-tuning language models with RLHF.