Cross-platform GUI agents frequently fail because high-quality, executable multi-platform trajectories are scarce and joint training mixes platform-specific behaviors. UI-MOPD addresses this by aligning the supervision distribution with the agent's deployment states: it supplies platform-conditioned, on-policy distilled guidance from platform-specific teachers while training a single shared policy to operate across desktop and mobile environments. The result is a practical compromise between retaining past capabilities and adapting to new platforms.
Key Findings
- Introduces Uni-GUI, a curated cross-platform GUI interaction dataset to broaden platform coverage and provide executable trajectories; this reduces reliance on single-platform corpora and supplies examples for continual updates.
- Proposes platform-conditioned multi-teacher on-policy distillation: at runtime the framework selects a platform-specific teacher and distills its behavioral prior into a shared student policy, which reduces behavioral mixing and catastrophic forgetting across platforms.
- Demonstrates empirical trade-offs: UI-MOPD achieves reported task success rates of 38.2% on OSWorld and 12.0% on MobileWorld, showing measurable retention-plus-adaptation but leaving headroom for absolute performance improvements.
- Designed to work in a continual-learning setup: the method incrementally incorporates new-platform data while using teacher guidance to preserve previously learned platform-specific skills.
Who it's for and trade-offs
Great fit if you are researching GUI automation agents, continual learning for interactive systems, or cross-platform adaptation techniques and need a reproducible dataset plus a distillation-based training recipe. Look elsewhere if you require high out-of-the-box success rates on complex mobile tasks (reported mobile performance is lower), have extremely limited compute or cannot provide platform-specific teacher models. The approach reduces catastrophic forgetting but depends on the availability and quality of platform-specific teachers and on-policy rollouts for effective distillation.
Where it fits
UI-MOPD sits between behavior-cloning-only GUI agents (which suffer from off-trajectory supervision gaps) and full multi-agent ensembles: it centralizes inference into a single student model while leveraging multiple teacher policies during training to keep platform priors distinct.
Method sketch
The pipeline conditions the student on platform type and alternates supervised fine-tuning on teacher traces with on-policy distillation from dynamically selected teachers during rollouts, letting the student learn to complete tasks from realistic, policy-induced states while preserving platform-specific behaviors.