Qwen3-VL-8B-Thinking-UI-MOPD-Student

Qwen3-VL-8B-Thinking-UI-MOPD-Student

This is the Student model from the UI-MOPD project — a cross-platform GUI agent trained via multi-teacher on-policy distillation for continual GUI agent learning.

Model Description

Qwen3-VL-8B-Thinking-UI-MOPD-Student is trained from Qwen3-VL-8B-Thinking using the UI-MOPD framework. It learns from two platform-specific 32B teachers (Desktop Teacher and Mobile Teacher) through reinforcement learning with platform-conditioned distillation, achieving balanced cross-platform performance on both desktop and mobile environments.

Key Highlights

• Base Model: Qwen3-VL-8B-Thinking
• Training Method: Multi-teacher on-policy distillation with DAPO + platform-conditioned KL regularization
• Teachers: Qwen3-VL-32B-Thinking-Desktop-Teacher + Qwen3-VL-32B-Thinking-Mobile-Teacher
• OSWorld Performance: 38.2% task success rate
• MobileWorld Performance: 12.0% task success rate

Training Details

This model is obtained in Stage 2 of the UI-MOPD training pipeline:

1. Stage 1: Supervised fine-tuning of Qwen3-VL-32B-Thinking on platform-specific data to produce a Desktop Teacher (46.3% on OSWorld) and a Mobile Teacher (16.2% on MobileWorld).
2. Stage 2 (This Model): The 8B student is trained with reinforcement learning (DAPO) combined with multi-teacher on-policy distillation. A platform-conditioned router selects the appropriate teacher based on the current environment, and adaptive KL masking prevents over-regularization.

Key Training Components

• Platform-Conditioned Routing: Routes each rollout to the corresponding platform-specific teacher
• K3 Estimator: Efficient single-sample KL divergence estimator
• Adaptive KL Masking: Removes teacher penalty when task reward is already sufficient

Performance

UI-MOPD achieves state-of-the-art balanced cross-platform performance, with +12.7% relative improvement on OSWorld and +55.8% on MobileWorld compared to the base model.

GUI Grounding & Understanding

UI-MOPD preserves GUI grounding and visual understanding capabilities while improving interactive task performance.

Intended Use

• Cross-platform GUI agent for executing tasks on both desktop (e.g., web browsing, file management) and mobile (e.g., app navigation, settings control) environments
• Research on continual learning and multi-platform adaptation for GUI agents

How to Use

from transformers import Qwen3VLForConditionalGeneration, AutoProcessor

model = Qwen3VLForConditionalGeneration.from_pretrained(
    "UI-MOPD/Qwen3-VL-8B-Thinking-UI-MOPD-Student",
    torch_dtype="auto",
    device_map="auto",
)
processor = AutoProcessor.from_pretrained("UI-MOPD/Qwen3-VL-8B-Thinking-UI-MOPD-Student")

Citation

@misc{lian2026uimopdmultiplatformonpolicydistillation,
      title={UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning}, 
      author={Niu Lian and Alan Chen and Zhehao Yu and Chengzhen Duan and Fazhan Liu and Hui Liu and Pei Fu and Jian Luan and Yaowei Wang and Shu-Tao Xia and Jinpeng Wang},
      year={2026},
      eprint={2607.04425},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2607.04425}, 
}

Related Resources

• Project Page: https://elisspectre.github.io/UI-MOPD/
• Code: https://github.com/EliSpectre/UI-MOPD
• Paper: UI-MOPD (arXiv)
• Desktop Teacher: UI-MOPD/Qwen3-VL-32B-Thinking-Desktop-Teacher