DanceGRPO: Unleashing GRPO on Visual Generation
Zeyue Xue, Jie Wu, Yu Gao, Fangyuan Kong, Lingting Zhu, Mengzhao Chen, Zhiheng Liu, Wei Liu, Qiushan Guo, Weilin Huang, Ping Luo
2025-05-13
Summary
This paper talks about DanceGRPO, a new system that uses reinforcement learning to make AI better at creating visuals, like images and videos, across many different types of tasks and models.
What's the problem?
The problem is that making high-quality visuals with AI can be tricky, especially when you want the system to work well in lots of different situations and with different goals. Existing methods often struggle to stay stable and consistent, especially for things like video generation.
What's the solution?
The researchers developed DanceGRPO, a unified framework that uses reinforcement learning to guide the AI as it creates visuals. This approach works well with different models and reward systems, and it outperforms older methods in tests, especially by making video generation more stable and reliable.
Why it matters?
This matters because it helps AI create better and more consistent images and videos, which is important for entertainment, education, advertising, and any field where visual content is used.
Abstract
DanceGRPO is a unified RL framework that enhances visual generation across different paradigms, tasks, models, and reward systems, outperforming baselines in benchmarks and improving video generation stability.