UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based
  Mobile GUI Agents
In this paper, we introduce UI-Genie, a self-improving framework addressing
two key challenges in GUI agents: verification of trajectory outcome is
challenging and high-quality training data are not scalable. These challenges
are addressed by a reward model and a self-improving pipeline, respectively.
The reward model, UI-Genie-RM, features an image-text interleaved architecture
that efficiently pro- cesses historical context and unifies action-level and
task-level rewards. To sup- port the training of UI-Genie-RM, we develop
deliberately-designed data genera- tion strategies including rule-based
verification, controlled trajectory corruption, and hard negative mining. To
address the second challenge, a self-improvement pipeline progressively expands
solvable complex GUI tasks by enhancing both the agent and reward models
through reward-guided exploration and outcome verification in dynamic
environments. For training the model, we generate UI- Genie-RM-517k and
UI-Genie-Agent-16k, establishing the first reward-specific dataset for GUI
agents while demonstrating high-quality synthetic trajectory gen- eration
without manual annotation. Experimental results show that UI-Genie achieves
state-of-the-art performance across multiple GUI agent benchmarks with three
generations of data-model self-improvement. We open-source our complete
framework implementation and generated datasets to facilitate further research
in https://github.com/Euphoria16/UI-Genie.
683698f3c32e462c40a9188f
Han Xiao
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HanXiao
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Guozhi Wang
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guozhi wang
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Yuxiang Chai
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Yuxiang Chai
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Zimu Lu
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Zimu Lu
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Weifeng Lin
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Weifeng Lin
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Hao He
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Lue Fan
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Liuyang Bian
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bian liuyang
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Rui Hu
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HURUI
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Liang Liu
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Yafei Wen
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Aojun Zhou
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Hongsheng Li
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Hongsheng LI
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UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based
  Mobile GUI Agents
In this paper, we introduce UI-Genie, a self-improving framework addressing
two key challenges in GUI agents: verification of trajectory outcome is
challenging and high-quality training data are not scalable. These challenges
are addressed by a reward model and a self-improving pipeline, respectively.
The reward model, UI-Genie-RM, features an image-text interleaved architecture
that efficiently pro- cesses historical context and unifies action-level and
task-level rewards. To sup- port the training of UI-Genie-RM, we develop
deliberately-designed data genera- tion strategies including rule-based
verification, controlled trajectory corruption, and hard negative mining. To
address the second challenge, a self-improvement pipeline progressively expands
solvable complex GUI tasks by enhancing both the agent and reward models
through reward-guided exploration and outcome verification in dynamic
environments. For training the model, we generate UI- Genie-RM-517k and
UI-Genie-Agent-16k, establishing the first reward-specific dataset for GUI
agents while demonstrating high-quality synthetic trajectory gen- eration
without manual annotation. Experimental results show that UI-Genie achieves
state-of-the-art performance across multiple GUI agent benchmarks with three
generations of data-model self-improvement. We open-source our complete
framework implementation and generated datasets to facilitate further research
in https://github.com/Euphoria16/UI-Genie.
UI-Genie framework addresses GUI agent challenges through a reward model with image-text architecture and a self-improvement pipeline, achieving state-of-the-art performance on multiple benchmarks.
image-text interleaved architecture GUI agents reward model self-improving pipeline rule-based verification controlled trajectory corruption hard negative mining reward-guided exploration outcome verification dynamic environments synthetic trajectory generation