OneReward: Unified Mask-Guided Image Generation via Multi-Task Human
  Preference Learning
Authors: name: Yuan Gong, name: Xionghui Wang, name: Jie Wu, name: Shiyin Wang, name: Yitong Wang, name: Xinglong Wu

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 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