GDPO Explained: NVIDIA Fixes GRPO for LLM Reinforcement Learning

NVIDIA recently introduced GDPO in a paper titled GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL Optimization. GDPO is a new reinforcement learning algorithm designed to fix GRPO’s limitations in multi-reward LLM training. In this video, we explain how GDPO works, why standard GRPO fails with multiple rewards, and how reward-decoupled normalization improves advantage estimation and model performance. Written Review - https://aipapersacademy.com/gdpo/ Paper - https://arxiv.org/abs/2601.05242 Code - https://github.com/NVlabs/GDPO GRPO Deep Dive - https://aipapersacademy.com/deepseekmath-grpo/ ___________________ 🔔 Subscribe for more AI paper reviews! 📩 Join the newsletter → https://aipapersacademy.com/newsletter/ Patreon - https://www.patreon.com/aipapersacademy The video was edited using VideoScribe - https://tidd.ly/44TZEiX ___________________ Chapters: 0:00 Introduction 1:51 GRPO Recap 3:30 Multi-Reward GRPO 4:30 GRPO Reward Collapse 6:00 GDPO's Fix 7:26 GDPO Results