I believe that if the reader is familiar with PPO, they will immediately understand GRPO as well.

I‘ve heard people say that GRPO giving a zero gradient in cases where neither the current sample nor the group scores give any reward is advantageous for optimization. It avoids killing your base model with low signal to noise updates, that can be a problem in PPO where the critic usually causes a non-zero gradient even for samples where one would rather be like „problem too hard for now, skip.“

I‘d be curious to hear you lay out your thoughts though!