Datasets:

PrentisAI
/

GroundGUI_RL

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 (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

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).