DanceGRPO: Unleashing GRPO on Visual Generation
Authors: name: Zeyue Xue; name: Jie Wu; name: Yu Gao; name: Fangyuan Kong; name: Lingting Zhu; name: Mengzhao Chen; name: Zhiheng Liu; name: Wei Liu; name: Qiushan Guo; name: Weilin Huang; name: Ping Luo
Published: 2025-05-12 17:59:34

Abstract: Recent breakthroughs in generative models-particularly diffusion models and
rectified flows-have revolutionized visual content creation, yet aligning model
outputs with human preferences remains a critical challenge. Existing
reinforcement learning (RL)-based methods for visual generation face critical
limitations: incompatibility with modern Ordinary Differential Equations
(ODEs)-based sampling paradigms, instability in large-scale training, and lack
of validation for video generation. This paper introduces DanceGRPO, the first
unified framework to adapt Group Relative Policy Optimization (GRPO) to visual
generation paradigms, unleashing one unified RL algorithm across two generative
paradigms (diffusion models and rectified flows), three tasks (text-to-image,
text-to-video, image-to-video), four foundation models (Stable Diffusion,
HunyuanVideo, FLUX, SkyReel-I2V), and five reward models (image/video
aesthetics, text-image alignment, video motion quality, and binary reward). To
our knowledge, DanceGRPO is the first RL-based unified framework capable of
seamless adaptation across diverse generative paradigms, tasks, foundational
models, and reward models. DanceGRPO demonstrates consistent and substantial
improvements, which outperform baselines by up to 181% on benchmarks such as
HPS-v2.1, CLIP Score, VideoAlign, and GenEval. Notably, DanceGRPO not only can
stabilize policy optimization for complex video generation, but also enables
generative policy to better capture denoising trajectories for Best-of-N
inference scaling and learn from sparse binary feedback. Our results establish
DanceGRPO as a robust and versatile solution for scaling Reinforcement Learning
from Human Feedback (RLHF) tasks in visual generation, offering new insights
into harmonizing reinforcement learning and visual synthesis. The code will be
released.