GRPO++ explores the Group Relative Policy Optimization (GRPO) algorithm, which is central to training reasoning models in reinforcement learning (RL). While GRPO is favored for its simplicity and efficiency, it has some hidden issues that can complicate RL training at scale. The article emphasizes recent research aimed at overcoming these shortcomings, detailing various techniques and best practices to optimize GRPO for effective RL training. The piece breaks down the different types of RL training used for large language models (LLMs). Reinforcement Learning from Human Feedback (RLHF) focuses on aligning models with human preferences, making it more suited for chat applications. In contrast, Reinforcement Learning with Verifiable Rewards (RLVR) is more relevant for reasoning tasks, relying on either known answers or rule-based verification for correctness. The article stresses the importance of creating a verifiable dataset, particularly in domains like math and coding, where outputs can be assessed against established criteria. A significant point made is how reasoning models differ from standard LLMs. They possess the capacity to think through prompts, leading to sophisticated behaviors like problem decomposition and solution critique. The process of RLVR encourages models to explore various reasoning paths, often resulting in longer and more complex outputs as they learn from their interactions with the RL environment. The discussion includes insights from the RL-Zero setup, which has shown that LLMs can develop reasoning capabilities through RL alone, without any prior supervised fine-tuning. This setup demonstrates the potential for models to reach "Aha moments," where they begin to critically evaluate their own responses, enhancing their reasoning skills over time.