Sample By Step, Optimize By Chunk: Chunk-Level GRPO For Text-to-Image
  Generation
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.
68feef306cdff8b857f471c6
Yifu Luo
68feef306cdff8b857f471c7
Penghui Du
claimed_verified
2025-10-27T10:25:20.740Z
647076467fd7ecdbd0ea03b1
/avatars/6e090ea5f88977c6f70544175094c2a6.svg
Penghui Du
eternaldolphin
user
eternaldolphin
68feef306cdff8b857f471c8
Bo Li
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2025-10-28T15:38:03.434Z
68fed13597cae9c0f484ebed
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libo
libo31
user
libo31
68feef306cdff8b857f471c9
Sinan Du
68feef306cdff8b857f471ca
Tiantian Zhang
68feef306cdff8b857f471cb
Yongzhe Chang
68feef306cdff8b857f471cc
Kai Wu
claimed_verified
2025-10-27T10:25:12.953Z
68e741ea3edb0ff47e20084e
https://cdn-avatars.huggingface.co/v1/production/uploads/68e741ea3edb0ff47e20084e/OyBgFqcU4QWyPF_K58Gt5.jpeg
Wu Kai
KaiiWuu1993
user
KaiiWuu1993
68feef306cdff8b857f471cd
Kun Gai
68feef306cdff8b857f471ce
Xueqian Wang
Sample By Step, Optimize By Chunk: Chunk-Level GRPO For Text-to-Image
  Generation
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.
Chunk-GRPO, a chunk-level optimization approach for text-to-image generation, improves preference alignment and image quality by addressing inaccurate advantage attribution and neglecting temporal dynamics.
flow-matching-based text-to-image T2I Group Relative Policy Optimization GRPO chunk-level temporal dynamics policy optimization weighted sampling strategy