Training
PPO (Proximal Policy Optimization)
A policy-gradient reinforcement learning algorithm, published by OpenAI researchers in 2017, that improves a policy in small, stable steps by clipping its objective so no single update moves too far.
In depth
A policy-gradient reinforcement learning algorithm, published by OpenAI researchers in 2017, that improves a policy in small, stable steps by clipping its objective so no single update moves too far. It became the standard optimizer for RLHF and was used to train InstructGPT. In that setting it runs alongside a learned value model (critic) and a KL penalty that keeps the model close to its starting point. That extra critic network is the memory cost that later methods like GRPO set out to remove.
Primary source: arxiv.org
Example
That extra critic network is the memory cost that later methods like GRPO set out to remove.
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FAQ
Common questions
What is PPO (Proximal Policy Optimization)?
A policy-gradient reinforcement learning algorithm, published by OpenAI researchers in 2017, that improves a policy in small, stable steps by clipping its objective so no single update moves too far.
Why does PPO (Proximal Policy Optimization) matter for AI developers?
PPO (Proximal Policy Optimization) sits in the Training part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about PPO (Proximal Policy Optimization)?
Developers Digest publishes tutorials and videos that cover Training topics including PPO (Proximal Policy Optimization). Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Training
PEFT (Parameter-Efficient Fine-Tuning)
A family of fine-tuning methods that adapt a large pretrained model by training only a small number of new or selected parameters while the original weights stay frozen.
Training
Post-Training
Everything done to a pretrained base model to turn it into a useful assistant.
Training
RLHF (Reinforcement Learning from Human Feedback)
A training technique that fine-tunes a model using human preference judgments.
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