This article discusses the Group Relative Policy Optimization (GRPO) algorithm and its applications in training reasoning models using reinforcement learning (RL). It outlines common techniques to address GRPO's limitations and compares different RL training approaches, particularly focusing on Reinforcement Learning with Verifiable Rewards (RLVR).