PPO (Proximal Policy Optimization)

A widely used reinforcement-learning algorithm that stabilizes policy updates with a clipped objective; common in RLHF fine-tuning stages.

PPO (Proximal Policy Optimization) is a reinforcement learning method that improves a policy while limiting how far each update can move from the previous one, using a clipped surrogate objective. That constraint reduces destructive large steps common in naive policy-gradient training.

In modern LLM stacks, PPO often appears in RLHF: a reward or preference model scores sampled completions, and PPO nudges the language model toward higher-scoring outputs as part of alignment after supervised learning and fine-tuning.