Flow-GRPO: Training Flow Matching Models via Online RL
Authors: name: Jie Liu, name: Gongye Liu, name: Jiajun Liang, name: Yangguang Li, name: Jiaheng Liu, name: Xintao Wang, name: Pengfei Wan, name: Di Zhang, name: Wanli Ouyang

We propose Flow-GRPO, the first method integrating online reinforcement
learning (RL) into flow matching models. Our approach uses two key strategies:
(1) an ODE-to-SDE conversion that transforms a deterministic Ordinary
Differential Equation (ODE) into an equivalent Stochastic Differential Equation
(SDE) that matches the original model's marginal distribution at all timesteps,
enabling statistical sampling for RL exploration; and (2) a Denoising Reduction
strategy that reduces training denoising steps while retaining the original
inference timestep number, significantly improving sampling efficiency without
performance degradation. Empirically, Flow-GRPO is effective across multiple
text-to-image tasks. For complex compositions, RL-tuned SD3.5 generates nearly
perfect object counts, spatial relations, and fine-grained attributes, boosting
GenEval accuracy from 63% to 95%. In visual text rendering, its accuracy
improves from 59% to 92%, significantly enhancing text generation.
Flow-GRPO also achieves substantial gains in human preference alignment.
Notably, little to no reward hacking occurred, meaning rewards did not increase
at the cost of image quality or diversity, and both remained stable in our
experiments.