What Is Proximal Policy Optimization (PPO)? The Algorithm Behind RLHF
The specific reinforcement learning algorithm most commonly used to actually implement RLHF, designed to update a model's behavior without changing it too drastically at once.
Updating behavior, but cautiously
Proximal policy optimization (PPO) is a reinforcement learning algorithm that updates a model's behavior toward higher-reward responses while specifically constraining how much the model's behavior is allowed to change in any single update — "proximal" referring to staying close to the previous behavior rather than jumping drastically.
Why constraining the update size matters here
An update that changes a model's behavior too drastically in one step risks destabilizing training or causing the model to overcorrect and get noticeably worse rather than better. PPO's core innovation is capping how large a behavior change any single update can cause, keeping training more stable and predictable.
Where PPO fits into RLHF
PPO is the specific algorithm most commonly used to actually implement the reinforcement learning update step in RLHF — the reward model's score feeds into PPO, which then calculates a cautious, bounded update to the model's behavior based on that score.
Frequently Asked Questions
What is PPO?
Proximal policy optimization — a reinforcement learning algorithm that updates a model's behavior toward higher reward while specifically constraining how much that behavior is allowed to change in any single update, keeping training stable.
How is PPO related to RLHF?
PPO is the specific algorithm most commonly used to implement RLHF's reinforcement learning update step — a reward model's score feeds into PPO, which calculates a cautious, bounded behavior update based on it.