UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning

Hugging Face Daily Papers Papers

Summary

This paper introduces UI-MOPD, a method combining multi-teacher on-policy distillation with continual learning for training GUI agents across multiple platforms, along with the Uni-GUI dataset. It achieves task success rates of 38.2% and 12.0% on OSWorld and MobileWorld respectively, demonstrating effective cross-platform capability retention and adaptation.

Recent advances in multimodal foundation models and agent systems have driven GUI agents from single-platform task execution toward cross-platform interaction. However, building multi-platform GUI agents remains challenging. On one hand, high-quality and executable cross-platform interaction trajectories are still scarce, and existing data often suffer from limited platform coverage. On the other hand, different platforms exhibit distinct interaction conventions, making joint or continual training prone to behavioral pattern mixing, platform-specific capability degradation, and catastrophic forgetting. To address these challenges, we construct Uni-GUI, a high-quality cross-platform GUI interaction dataset, and propose UI-MOPD, the first method that incorporates multi-teacher on-policy distillation into continual learning for GUI agents. UI-MOPD dynamically selects a platform-specific teacher according to the current environment and transfers platform-specific behavioral priors to a shared policy through platform-conditioned distillation, enabling adaptation to new platforms while preserving capabilities on existing ones. Experiments on OSWorld and MobileWorld show that UI-MOPD achieves task success rates of 38.2% and 12.0%, respectively, demonstrating its effectiveness in balancing cross-platform capability retention and new-platform adaptation. Project page: https://elispectre.github.io/UI-MOPD/.
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Abstract

Uni-GUI dataset and UI-MOPD method enable cross-platform GUI agent training by addressing limited data and platform-specific capability degradation through multi-teacher on-policy distillation.

Recent advances inmultimodal foundation modelsandagent systemshave drivenGUI agentsfrom single-platform task execution towardcross-platform interaction. However, building multi-platformGUI agentsremains challenging. On one hand, high-quality and executablecross-platform interactiontrajectories are still scarce, and existing data often suffer from limited platform coverage. On the other hand, different platforms exhibit distinct interaction conventions, making joint or continual training prone tobehavioral pattern mixing, platform-specific capability degradation, andcatastrophic forgetting. To address these challenges, we construct Uni-GUI, a high-quality cross-platform GUI interaction dataset, and propose UI-MOPD, the first method that incorporatesmulti-teacher on-policy distillationintocontinual learningforGUI agents. UI-MOPD dynamically selects a platform-specific teacher according to the current environment and transfers platform-specific behavioral priors to a shared policy throughplatform-conditioned distillation, enabling adaptation to new platforms while preserving capabilities on existing ones. Experiments on OSWorld and MobileWorld show that UI-MOPD achieves task success rates of 38.2% and 12.0%, respectively, demonstrating its effectiveness in balancing cross-platform capability retention and new-platform adaptation. Project page: https://elispectre.github.io/UI-MOPD/.

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