Reinforcement Learning: The Surrogate Objective Function in PPO
ShawnHymel · x · 2026-08-28
Part of a reinforcement learning tutorial series, this post focuses on the Surrogate Objective Function within Proximal Policy Optimization (PPO). It explains how this function enables Actor-Critic methods to practically use batched rollout data, improving sample efficiency, maintaining training stability, and reducing hyperparameter sensitivity.
Related event: New Tutorial Explains the Surrogate Objective in RL and PPO(2 posts)→
More from Research
- CarNet Matches Spherical-Harmonics Accuracy in Cartesian Space for Atomic Simulations — bravo_abad · 2026-08-29
- TRACES Leaderboard Evaluates AI Discovery Capabilities; Kimi and GLM Top Charts — SimonShaoleiDu · 2026-08-29
- Simplicity as a Metric: Fewer Hyperparameters Makes for a Better Method — Muennighoff · 2026-08-29
- MLatom 3.25: Ultra-efficient fine-tuning of ML potentials on CPU — PavloDral · 2026-08-29
- LEAP panel report: Author predicted 35% on FrontierMath, actual 41% — dioscuri · 2026-08-29
- NeurIPS Sim2Science workshop extends submission deadline to Sep 2, 2026 — _rdgao · 2026-08-29