Sample By Step, Optimize By Chunk: Chunk-Level GRPO For Text-to-Image
  Generation
Authors: name: Yifu Luo, name: Penghui Du, name: Bo Li, name: Sinan Du, name: Tiantian Zhang, name: Yongzhe Chang, name: Kai Wu, name: Kun Gai, name: Xueqian Wang
Organization: Kolors Team, Kuaishou Technology

Group Relative Policy Optimization (GRPO) has shown strong potential for
flow-matching-based text-to-image (T2I) generation, but it faces two key
limitations: inaccurate advantage attribution, and the neglect of temporal
dynamics of generation. In this work, we argue that shifting the optimization
paradigm from the step level to the chunk level can effectively alleviate these
issues. Building on this idea, we propose Chunk-GRPO, the first chunk-level
GRPO-based approach for T2I generation. The insight is to group consecutive
steps into coherent 'chunk's that capture the intrinsic temporal dynamics of
flow matching, and to optimize policies at the chunk level. In addition, we
introduce an optional weighted sampling strategy to further enhance
performance. Extensive experiments show that ChunkGRPO achieves superior
results in both preference alignment and image quality, highlighting the
promise of chunk-level optimization for GRPO-based methods.