OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse
  Task Synthesis
Graphical User Interface (GUI) agents powered by Vision-Language Models
(VLMs) have demonstrated human-like computer control capability. Despite their
utility in advancing digital automation, a critical bottleneck persists:
collecting high-quality trajectory data for training. Common practices for
collecting such data rely on human supervision or synthetic data generation
through executing pre-defined tasks, which are either resource-intensive or
unable to guarantee data quality. Moreover, these methods suffer from limited
data diversity and significant gaps between synthetic data and real-world
environments. To address these challenges, we propose OS-Genesis, a novel GUI
data synthesis pipeline that reverses the conventional trajectory collection
process. Instead of relying on pre-defined tasks, OS-Genesis enables agents
first to perceive environments and perform step-wise interactions, then
retrospectively derive high-quality tasks to enable trajectory-level
exploration. A trajectory reward model is then employed to ensure the quality
of the generated trajectories. We demonstrate that training GUI agents with
OS-Genesis significantly improves their performance on highly challenging
online benchmarks. In-depth analysis further validates OS-Genesis's efficiency
and its superior data quality and diversity compared to existing synthesis
methods. Our codes, data, and checkpoints are available at
https://qiushisun.github.io/OS-Genesis-Home/{OS-Genesis Homepage}.
67720d79b3163a95a653baaf
Qiushi Sun
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2025-01-02T10:19:16.620Z
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Qiushi
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QiushiSun
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Kanzhi Cheng
claimed_verified
2025-01-07T08:42:48.096Z
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Kanzhi Cheng
user
cckevinn
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Zichen Ding
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2025-01-02T10:19:18.679Z
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Zichen Ding
user
heroding77
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Chuanyang Jin
claimed_verified
2025-01-03T14:05:28.148Z
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Chuanyang Jin
user
Chuanyang-Jin
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Yian Wang
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Fangzhi Xu
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2025-01-03T14:05:20.429Z
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Fangzhi Xu
user
xufangzhi
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Zhenyu Wu
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Chengyou Jia
claimed_verified
2025-01-03T14:05:30.296Z
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ChengyouJia
user
ChengyouJia
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Liheng Chen
claimed_verified
2025-01-03T14:05:32.287Z
6561824484a9fbe322b9abc3
https://cdn-avatars.huggingface.co/v1/production/uploads/6561824484a9fbe322b9abc3/omS3D6PzGBD7kc4S7bOIO.png
LIHENG CHEN
user
Lemaqwq
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Zhoumianze Liu
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Ben Kao
67720d79b3163a95a653baba
Guohao Li
claimed_verified
2025-09-06T10:58:02.104Z
6338790e76421c054310c96b
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Guohao Li
user
lightaime
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Junxian He
67720d79b3163a95a653babc
Yu Qiao
67720d79b3163a95a653babd
Zhiyong Wu
OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse
  Task Synthesis
Graphical User Interface (GUI) agents powered by Vision-Language Models
(VLMs) have demonstrated human-like computer control capability. Despite their
utility in advancing digital automation, a critical bottleneck persists:
collecting high-quality trajectory data for training. Common practices for
collecting such data rely on human supervision or synthetic data generation
through executing pre-defined tasks, which are either resource-intensive or
unable to guarantee data quality. Moreover, these methods suffer from limited
data diversity and significant gaps between synthetic data and real-world
environments. To address these challenges, we propose OS-Genesis, a novel GUI
data synthesis pipeline that reverses the conventional trajectory collection
process. Instead of relying on pre-defined tasks, OS-Genesis enables agents
first to perceive environments and perform step-wise interactions, then
retrospectively derive high-quality tasks to enable trajectory-level
exploration. A trajectory reward model is then employed to ensure the quality
of the generated trajectories. We demonstrate that training GUI agents with
OS-Genesis significantly improves their performance on highly challenging
online benchmarks. In-depth analysis further validates OS-Genesis's efficiency
and its superior data quality and diversity compared to existing synthesis
methods. Our codes, data, and checkpoints are available at
https://qiushisun.github.io/OS-Genesis-Home/{OS-Genesis Homepage}.
OS-Genesis is a novel GUI data synthesis pipeline that enhances the training of GUI agents by reversing the trajectory collection process to improve data quality and diversity.
Vision-Language Models GUI agents trajectory data human supervision synthetic data generation data diversity step-wise interactions trajectory reward model online benchmarks