PPO
Proximal Policy Optimization is a highly stable reinforcement learning algorithm that restricts how much a model's policy can change in a single update step.
Think of It Like This
Like a golf instructor forcing you to fix your swing in tiny, safe increments rather than trying a wildly different technique on every single shot.
PPO optimizes a clipped surrogate objective function. By clipping the ratio between the new and old policy, it prevents destructively large gradient updates that could crash the learning process. Due to its remarkable balance of sample efficiency and reliability, PPO is the industry standard for fine-tuning LLMs via RLHF.