PPO
Proximal Policy Optimization - a stable and efficient policy gradient algorithm widely used in RLHF for training LLMs.
Related Concepts
- Policy Gradient: Explore how Policy Gradient relates to PPO
- RLHF: Explore how RLHF relates to PPO
- Actor-Critic: Explore how Actor-Critic relates to PPO
Why It Matters
Understanding PPO is crucial for anyone working with reinforcement learning. This concept helps build a foundation for more advanced topics in AI and machine learning.
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This term is part of the comprehensive AI/ML glossary. Explore related terms to deepen your understanding of this interconnected field.
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Related Terms
Actor-Critic
RL architecture with two components: an actor (policy) that selects actions and a critic (value function) that evaluates them.
Policy Gradient
RL methods that directly optimize the policy by computing gradients of expected reward with respect to policy parameters.
RLHF
Reinforcement Learning from Human Feedback - training models using human preferences to align behavior with human values.