Mechanism

Minimalist RLVR method that strips away surrogate ratio clipping, reference-model KL divergence, and critic networks altogether, directly optimizing the group-normalized REINFORCE objective with dynamic thresholding of zero-variance prompts.

Practical Notes

Useful when memory constraints forbid keeping πref\pi_{\text{ref}} and πθold\pi_{\theta_{\text{old}}} alongside πθ\pi_\theta.

References

  1. GPG: A Simple and Strong Reinforcement Learning Baseline for Model Reasoning ↗

    Xiangxiang Chu, Hailang Huang, Xiao Zhang, Fei Wei, Yong Wang · 2025