Reinforcement Learning · advanced · concept 189 of 311 · Updated Nov 4, 2026
GRPO (Group Relative Policy Optimization)
A reinforcement learning algorithm that simplifies PPO by dropping the value network: it samples a group of responses per prompt and scores each one against the group average. Introduced in DeepSeekMath and used to train DeepSeek-R1, it became a standard recipe for training reasoning models on verifiable rewards like math and unit tests.
Top AI contributors on this
- Demis HassabisGoogle DeepMind (chair) / Isomorphic Labs
- Pieter AbbeelAmazon / UC Berkeley
- David SilverIneffable Intelligence / UCL
- John SchulmanThinking Machines Lab
- Sergey LevineUC Berkeley / Physical Intelligence
- Chelsea FinnStanford University / Physical Intelligence
Key terms
Group baselineNo value networkVerifiable rewardPolicy gradientRollouts
Learn these first
Where you meet it in the real world
Training reasoning models, math and code post-training, open-weight R1 replications
Videos
▶ Stanford CME295 Transformers & LLMs | Autumn 2025 | Lecture 6 - LLM Reasoning ↗
Stanford Online · YouTube
Guides and articles
GRPO Trainer · Hugging Face ↗
Hugging Face
Courses, papers, and more