GRPO-Guard: Mitigating Implicit Over-Optimization in Flow Matching via
  Regulated Clipping
Authors: name: Jing Wang; name: Jiajun Liang; name: Jie Liu; name: Henglin Liu; name: Gongye Liu; name: Jun Zheng; name: Wanyuan Pang; name: Ao Ma; name: Zhenyu Xie; name: Xintao Wang; name: Meng Wang; name: Pengfei Wan; name: Xiaodan Liang
Published: 2025-10-25 14:51:17

Abstract: Recently, GRPO-based reinforcement learning has shown remarkable progress in
optimizing flow-matching models, effectively improving their alignment with
task-specific rewards. Within these frameworks, the policy update relies on
importance-ratio clipping to constrain overconfident positive and negative
gradients. However, in practice, we observe a systematic shift in the
importance-ratio distribution-its mean falls below 1 and its variance differs
substantially across timesteps. This left-shifted and inconsistent distribution
prevents positive-advantage samples from entering the clipped region, causing
the mechanism to fail in constraining overconfident positive updates. As a
result, the policy model inevitably enters an implicit over-optimization
stage-while the proxy reward continues to increase, essential metrics such as
image quality and text-prompt alignment deteriorate sharply, ultimately making
the learned policy impractical for real-world use. To address this issue, we
introduce GRPO-Guard, a simple yet effective enhancement to existing GRPO
frameworks. Our method incorporates ratio normalization, which restores a
balanced and step-consistent importance ratio, ensuring that PPO clipping
properly constrains harmful updates across denoising timesteps. In addition, a
gradient reweighting strategy equalizes policy gradients over noise conditions,
preventing excessive updates from particular timestep regions. Together, these
designs act as a regulated clipping mechanism, stabilizing optimization and
substantially mitigating implicit over-optimization without relying on heavy KL
regularization. Extensive experiments on multiple diffusion backbones (e.g.,
SD3.5M, Flux.1-dev) and diverse proxy tasks demonstrate that GRPO-Guard
significantly reduces over-optimization while maintaining or even improving
generation quality.