OneReward: Unified Mask-Guided Image Generation via Multi-Task Human
  Preference Learning
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
68b13835b19c5400014849e1
Yuan Gong
68b13835b19c5400014849e2
Xionghui Wang
claimed_verified
2025-08-29T08:01:36.765Z
645df271689b17a1078ee77a
/avatars/f02399e5149da269b394114b3ff43ef8.svg
Xionghui Wang
user
XionghuiWang
68b13835b19c5400014849e3
Jie Wu
claimed_verified
2025-08-29T09:12:18.507Z
6381c5d63680a7cf34e08ca9
/avatars/731467e2d80d0ae163c4a00a9e3ff9e5.svg
wujie10558@gmail.com
user
wujie10
68b13835b19c5400014849e4
Shiyin Wang
68b13835b19c5400014849e5
Yitong Wang
68b13835b19c5400014849e6
Xinglong Wu
OneReward: Unified Mask-Guided Image Generation via Multi-Task Human
  Preference Learning
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
A unified reinforcement learning framework using a single vision-language model enhances generative capabilities across multiple tasks without task-specific fine-tuning.
reinforcement learning vision-language model generative reward model multi-task generation mask-guided image generation image fill image extend object removal text rendering multi-task reinforcement learning Seedream 3.0 Fill