| --- |
| license: mit |
| language: |
| - en |
| task_categories: |
| - image-to-text |
| tags: |
| - computer_use |
| - gui-grounding |
| - reinforcement-learning |
| - GroundCUA |
| size_categories: |
| - 10K<n<100K |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-*.parquet |
| --- |
| |
| # GroundGUI RL Dataset |
|
|
| A reinforcement-learning (RLOO) corpus for desktop GUI grounding: 12,600 |
| prompts over 11,205 screenshots from 87 desktop applications. Each prompt is a |
| screenshot plus a natural-language instruction, and the reward is whether the |
| predicted click lands inside the target element's bounding box. |
|
|
| All screenshots, instructions and boxes come from |
| [GroundCUA](https://huggingface.co/datasets/ServiceNow/GroundCUA) (MIT). This |
| release only selects and mixes samples and formats them as Qwen3-VL |
| `computer_use` tool-call prompts. None of its screenshots is identical to |
| any UI-Vision image. |
|
|
| ## Usage |
|
|
| ```python |
| import json |
| from datasets import load_dataset |
| |
| ds = load_dataset("PrentisAI/GroundGUI_RL", split="train") |
| row = ds[0] |
| img = row["images"][0] # PIL.Image |
| x1, y1, x2, y2 = row["solution"]["arguments"]["coordinate"] # target box, absolute pixels |
| extra = json.loads(row["additional_paras"]) # JSON string: image_size, app, slice, ... |
| ``` |
|
|
| `messages` holds the system, user and assistant turns. The assistant turn is |
| a reference answer: the box centre in 0–1000 normalised coordinates. Compute |
| the reward from `solution`. |
|
|
| ## License |
|
|
| MIT, inherited from GroundCUA (see `LICENSE`). |
|
|
| ## Citation |
|
|
| This RL corpus is introduced in [GroundGUI: GUI Grounding by High-Quality Data and Distillation-to-Reinforcement Learning](https://arxiv.org/abs/2610.19100). If you use it, please cite: |
|
|
| ```bibtex |
| @misc{xie2026groundguiguigroundinghighquality, |
| title={GroundGUI: GUI Grounding by High-Quality Data and Distillation-to-Reinforcement Learning}, |
| author={Qingsong Xie and Jiahao Tian and Jiacheng Tang and Zhifeng Li and Madalina Ciobanu and Navan Preet Singh and Qingqing Mao and Ritankar Das}, |
| year={2026}, |
| eprint={2610.19100}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.AI}, |
| url={https://arxiv.org/abs/2610.19100}, |
| } |
| ``` |
|
|
