TRL v1.0: Post-Training Library Built to Move with the Field

This post introduces Proximal Policy Optimization (PPO) as a new capability, detailing its algorithm and implementation. It explains how PPO improves training stability in deep reinforcement learning by limiting policy updates via a clipped surrogate objective function. The post includes practical coding examples and explanations of the clipped surrogate objective and ratio calculations.