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).

The article critiques reinforcement learning (RL) for its inefficiency and slow convergence, particularly highlighting the limitations of policy gradient methods. It proposes the principle of certainty equivalence as a more effective alternative for optimization, especially in reasoning models. The author questions whether the recent applications of RL in large language models truly represent progress or if there are better methods available.