我在读一篇关于分数中心化强化学习的论文，想在我的书里加些笔记。他们在附录中用一张很棒的表格总结了强化学习中各种基于重要性采样的训练与推理不匹配处理方法。在我看来，自 GRPO 兴起以来，这一直是强化学习算法研究最受关注的方向。

Was reading the score-centering RL paper to add some notes on it the my book, and they have a great table in the appendix on the different importance sampling based train-inference mismatch methods in RL. These have been the biggest focus of RL algorithm research since the GRPO takeoff, imo.