This is the Mobile SFT model from the UI-MOPD project — an 8B model supervised-fine-tuned on mobile GUI interaction data. This checkpoint is released as an auxiliary artifact to demonstrate the quality of the Uni-GUI SFT data on the 8B tier and to enable community work such as Model-Merge (TIES / DARE) with other 8B GUI agents.

Note: This model is not used in the UI-MOPD student training path. In the UI-MOPD pipeline, the 8B student is cold-started directly from the Qwen3-VL-8B-Thinking base and trained via on-policy distillation from the 32B teachers.

Model Description

Qwen3-VL-8B-Thinking-Mobile-SFT is fine-tuned from Qwen3-VL-8B-Thinking on mobile interaction trajectories from the Uni-GUI dataset. It is trained independently on the same mobile data used by the 32B Mobile Teacher, but at the 8B scale.

Key Highlights

Base Model: Qwen3-VL-8B-Thinking
Training Data: Mobile subset of Uni-GUI (~160K interaction steps across ~11.5K trajectories)
Training: Supervised fine-tuning (SFT) on mobile GUI interaction trajectories
Role: Auxiliary artifact — demonstrates Uni-GUI data quality on the 8B tier and enables Model-Merge experiments. Not used in the UI-MOPD student training path.

Relationship to the UI-MOPD Pipeline

The actual UI-MOPD training pipeline is:

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).
Stage 2: The 8B student is cold-started from Qwen3-VL-8B-Thinking base (not from any SFT checkpoint) and trained via DAPO (reinforcement learning) with multi-teacher on-policy distillation using platform-conditioned routing from the 32B teachers.

This 8B SFT model sits outside the distillation pipeline. It is trained independently on the same Uni-GUI mobile subset and released separately for the community.

Intended Use

This model is designed to:

Be used as a standalone mobile GUI agent for executing mobile tasks (e.g., app navigation, settings control, messaging)
Serve as a reference for evaluating the quality of the Uni-GUI SFT data on the 8B scale
Provide a base for Model-Merge / TIES / DARE experiments with other 8B GUI agents
Offer a baseline for comparing SFT-only vs. distillation-enhanced performance

How to Use

python

from transformers import Qwen3VLForConditionalGeneration, AutoProcessor

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

Citation

bibtex

@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
Mobile Teacher: UI-MOPD/Qwen3-VL-32B-Thinking-Mobile-Teacher
Student Model (cold-started from base, not from this SFT): UI-MOPD/Qwen3-VL-8B-Thinking-UI-MOPD-Student