Qwen3-VL-32B-Thinking-Desktop-Teacher
This is the Desktop Teacher model from the UI-MOPD project — a platform-specific expert trained for desktop GUI interaction tasks.
Model Description
Qwen3-VL-32B-Thinking-Desktop-Teacher is fine-tuned from Qwen3-VL-32B-Thinking on desktop interaction trajectories from the Uni-GUI dataset. It serves as the desktop-platform teacher in the UI-MOPD multi-teacher on-policy distillation framework.
Key Highlights
- Base Model: Qwen3-VL-32B-Thinking
- Training Data: Desktop subset of Uni-GUI (~160K interaction steps across ~11.5K trajectories)
- Role: Platform-specific teacher for desktop environments in the UI-MOPD distillation pipeline
- OSWorld Performance: 46.3% task success rate (vs. 41.0% base model)
Training Details
This model is obtained in Stage 1 of the UI-MOPD training pipeline:
- Stage 1 (This Model): Supervised fine-tuning of Qwen3-VL-32B-Thinking on desktop GUI interaction trajectories from Uni-GUI to produce a platform-specific desktop expert.
- Stage 2: The desktop teacher (this model) and a mobile teacher jointly guide a shared 8B student policy via multi-teacher on-policy distillation with platform-conditioned routing.
Performance
| Method | OSWorld |
|---|---|
| Qwen3-VL-32B-Thinking (base) | 41.0% |
| Desktop Teacher (this model) | 46.3% |
Intended Use
This model is designed to:
- Serve as a teacher model in the UI-MOPD distillation framework for training cross-platform GUI agents
- Be used as a standalone desktop GUI agent for executing computer tasks (e.g., web browsing, file management, application control)
How to Use
from transformers import Qwen3VLForConditionalGeneration, AutoProcessor
model = Qwen3VLForConditionalGeneration.from_pretrained(
"UI-MOPD/Qwen3-VL-32B-Thinking-Desktop-Teacher",
torch_dtype="auto",
device_map="auto",
)
processor = AutoProcessor.from_pretrained("UI-MOPD/Qwen3-VL-32B-Thinking-Desktop-Teacher")
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)