PPO & Modern Policy Optimization
Trust regions, the clipped surrogate objective, and Generalized Advantage Estimation — the algorithm behind RLHF
Learn why unconstrained policy gradient steps can destroy a policy in one update, how TRPO's trust region fixes this with a constrained optimization problem, how PPO approximates that trust region cheaply with a clipped surrogate objective, how Generalized Advantage Estimation trades bias for variance in the advantage estimate, and the implementation details — advantage normalization, minibatch epochs, value clipping, KL early stopping — that determine whether a PPO run actually converges.
Practice questions (6)
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Diagnose and Fix a Policy Collapse During Vanilla Policy Gradient Training
Advanced · Free -
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Compute the Clipped Surrogate Objective for a Batch of Samples
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Choose a GAE Lambda for Two Different Reward Structures
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Debug an Unstable PPO Run from Its Implementation Details
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Map PPO's Components onto an RLHF Fine-Tuning Pipeline
Advanced