pith:6OSZOQEC
CoCoEdit: Content-Consistent Image Editing via Region Regularized Reinforcement Learning
Region regularized reinforcement learning trains image editing models to preserve non-edited areas while maintaining edit quality.
arxiv:2602.14068 v2 · 2026-02-15 · cs.CV
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Claims
Applying CoCoEdit to Qwen-Image-Edit and FLUX-Kontext, we achieve not only competitive editing scores with state-of-the-art models, but also significantly better content consistency, measured by PSNR/SSIM metrics and human subjective ratings.
The region-based regularizer successfully balances preservation of non-edited areas with editing strength without introducing new artifacts or degrading overall quality, relying on the assumption that the combined reward signals accurately reflect desired behavior across diverse images.
CoCoEdit applies region-regularized RL with pixel similarity and MLLM rewards to achieve competitive editing quality alongside significantly improved content consistency on models like Qwen-Image-Edit and FLUX-Kontext.
Receipt and verification
| First computed | 2026-05-17T23:39:16.145400Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/6OSZOQECNY7TTUZYNCGURXAPOV \
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Canonical record JSON
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