PPO is a reinforcement learning algorithm that optimizes an agent's policy by clipping the update step to prevent dangerously large changes, striking a balance between learning speed and stability. It is the algorithm behind RLHF used to train ChatGPT and Claude to be helpful assistants: a reward model scores responses, and PPO updates the language model to produce higher-scored outputs. PPO is popular because it is relatively simple to implement and tune compared to earlier RL algorithms.