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.