What is GRPO?

Definition

GRPO

Group Relative Policy Optimization (GRPO) is a parameter-efficient reinforcement learning algorithm used to align language models. Rather than relying on a separate reward model, GRPO evaluates model responses relative to a group of generated answers, reducing GPU overhead.

Detailed Deep Dive

Group Relative Policy Optimization (GRPO) is a reinforcement learning algorithm designed to align language models with human preferences without the extreme memory overhead of standard methods. Unlike PPO, which requires training and hosting a separate critic model to score states, GRPO samples a group of candidate outputs for a prompt and evaluates their rewards relative to the group average. This relative reward signal optimizes the model policy directly, saving substantial GPU memory.

Frequently Asked Questions

Q:What is the difference between GRPO and PPO?

Proximal Policy Optimization (PPO) requires training a separate critic/reward model to score outputs. GRPO calculates relative rewards within a group of outputs, saving significant memory.

Q:Which model popularized GRPO?

DeepSeek-Math and DeepSeek-R1 popularized GRPO for training large reasoning models.

Quick Facts

CategoryModel Training
Key ApplicationReasoning model alignment, RLHF pipeline scaling, and math/logic model training

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Related AI Terms

Alignment DPO RLHF

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