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OneReward: Unified Mask-Guided Image Generation via Multi-Task Human Preference Learning

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arxiv 2508.21066 v1 pith:ZRV3FT7L submitted 2025-08-28 cs.CV

OneReward: Unified Mask-Guided Image Generation via Multi-Task Human Preference Learning

Yuan Gong , Xionghui Wang , Jie Wu , Shiyin Wang , Yitong Wang , Xinglong Wu This is my paper
classification cs.CV
keywords modelevaluationgenerationimageonerewardfilllearningmask-guided
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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In this paper, we introduce OneReward, a unified reinforcement learning framework that enhances the model's generative capabilities across multiple tasks under different evaluation criteria using only \textit{One Reward} model. By employing a single vision-language model (VLM) as the generative reward model, which can distinguish the winner and loser for a given task and a given evaluation criterion, it can be effectively applied to multi-task generation models, particularly in contexts with varied data and diverse task objectives. We utilize OneReward for mask-guided image generation, which can be further divided into several sub-tasks such as image fill, image extend, object removal, and text rendering, involving a binary mask as the edit area. Although these domain-specific tasks share same conditioning paradigm, they differ significantly in underlying data distributions and evaluation metrics. Existing methods often rely on task-specific supervised fine-tuning (SFT), which limits generalization and training efficiency. Building on OneReward, we develop Seedream 3.0 Fill, a mask-guided generation model trained via multi-task reinforcement learning directly on a pre-trained base model, eliminating the need for task-specific SFT. Experimental results demonstrate that our unified edit model consistently outperforms both commercial and open-source competitors, such as Ideogram, Adobe Photoshop, and FLUX Fill [Pro], across multiple evaluation dimensions. Code and model are available at: https://one-reward.github.io

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Cited by 10 Pith papers

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    Edit-R1 builds a CoT-based reasoning reward model (RRM) via SFT and GCPO, then applies it with GRPO to improve image editing models such as FLUX.1-kontext.

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