Proximal Policy Optimization Algorithms
by OpenAI · free · Last verified 2026-03-17
PPO introduces a clipped surrogate objective that constrains policy update step sizes, achieving the stability of trust-region methods (TRPO) with the simplicity and scalability of first-order optimizers. It quickly became the dominant RL algorithm for training large language models with human feedback.
https://arxiv.org/abs/1707.06347 ↗A
A—Great
Adoption: A+Quality: A+Freshness: BCitations: A+Engagement: F
Specifications
- License
- Open Access
- Pricing
- free
- Capabilities
- policy-optimization, on-policy-training, continuous-control, rlhf-training
- Integrations
- Stable Baselines3, Hugging Face TRL (Transformer Reinforcement Learning), Ray RLlib, Tianshou, CleanRL
- Use Cases
- rl-training, llm-fine-tuning, game-playing, robotics-control
- API Available
- No
- Tags
- reinforcement-learning, ppo, policy-gradient, openai, training
- Added
- 2026-03-17
- Completeness
- 100%
Index Score
81.1Adoption
95
Quality
93
Freshness
60
Citations
98
Engagement
0