---
license: cc-by-4.0
task_categories:
  - image-text-to-text
  - visual-question-answering
language:
  - en
  - zh
pretty_name: InquireMobile
size_categories:
  - 10K<n<100K
tags:
  - mobile-agent
  - gui-agent
  - vlm
  - rl
  - interactive
---

# InquireMobile

Dataset for **InquireMobile: Teaching VLM-based Mobile Agent to Request Human
Assistance via Reinforcement Fine-Tuning** (ACL 2026).

InquireMobile is an interactive mobile GUI agent that *actively seeks human
confirmation at critical decision points* instead of acting autonomously. This
dataset contains:

- **InquireBench** evaluation tasks (instruction + category + intent), and
- the **training trajectories** used for supervised fine-tuning (SFT) and
  reinforcement fine-tuning (GRPO / RLVR).

> Project page: <https://bit-aqh.github.io/InquireMobile/homepage/>
> Paper: <https://aclanthology.org/2026.acl-long.1487/>

## Contents

The dataset is organized as:

- `data/` — all JSONL data files (see below).
- `images/` — screenshots referenced by the data files (see [Images](#images)).

All data files are line-delimited JSON (JSONL) unless noted otherwise.

### Evaluation tasks (InquireBench)

| File | Description |
|---|---|
| `tasks_en.jsonl`, `tasks_zh.jsonl` | Core evaluation task instructions (English / Chinese). |
| `tasks_en_extended.jsonl` | Extended English task set. |
| `tasks_{en,zh}_huawei.jsonl`, `tasks_{en,zh}_simulator.jsonl` | Task subsets for the Huawei device / simulator environments. |
| `tasks_zh_huawei_7.jsonl` | Huawei-device subset (7 tasks). |

Task schema:

```json
{
  "instruction": "Open WeChat, browse my Moments ...",
  "apps": ["wechat"],
  "category": "privacy",
  "language": "en",
  "need_login": true,
  "intension": "Open WeChat, browse my Moments ..."
}
```

### Training data

| File | Description |
|---|---|
| `interactive_sft.jsonl` | Stage-1 SFT trajectories (conversation format). |
| `interactive_grpo_401408.jsonl` | Stage-2 GRPO / RLVR trajectories (401408 max-pixels). |
| `general_grpo_5k.jsonl` | General GRPO training data (5k). |
| `sft_general.jsonl` | SFT data (general). |
| `sft_pos.jsonl` | SFT data (positive samples). |

### Instruction pool

| File | Description |
|---|---|
| `instructions.jsonl` | Raw instruction pool (Chinese + English), with optional `image` links to the instruction screenshots. |

GRPO / trajectory schema:

```json
{
  "goal": "...",
  "action": "...",
  "bbox": [x1, y1, x2, y2],
  "width": 1080,
  "height": 1920,
  "resized_width": 432,
  "resized_height": 768,
  "img_list": ["..."],
  "system": "...",
  "query": "..."
}
```

SFT schema:

```json
{
  "conversations": [
    {"from": "system", "value": "..."},
    {"from": "human", "value": "<image>..."},
    {"from": "gpt", "value": "..."}
  ],
  "images": ["..."]
}
```

## Images

Images are stored under `images/` and are referenced by **relative paths** from
the data files (relative to the dataset root).

| Directory | Description |
|---|---|
| `images/trajectory/` | Stage-2 GRPO rollout screenshots (UUID-named), referenced by `img_list` in the GRPO data. |
| `images/instruction/` | The 72 English instruction screenshots (`1.png`…`72.png`), referenced by the `images` field of the SFT data and by `interactive_added.jsonl`. |

The data files reference images by their basename (UUID) or number (`N.png`); map
these to the directories above when reproducing training/evaluation.

## Usage

```python
import json

with open("data/tasks_en.jsonl", encoding="utf-8") as f:
    tasks = [json.loads(line) for line in f]
```

Or download directly from Hugging Face:

```bash
huggingface-cli download PG23/InquireMobile --repo-type dataset --local-dir ./InquireMobile-data
```

## License

- **Data**: [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

## Citation

```bibtex
@inproceedings{ai-etal-2026-inquiremobile,
    title = "{I}nquire{M}obile: Teaching {VLM}-based Mobile Agent to Request Human Assistance via Reinforcement Fine-Tuning",
    author = "Ai, Qihang  and
      Bu, Pi  and
      Cao, Yue  and
      Wang, Yingyao  and
      Gu, Jihao  and
      Xing, Jingxuan  and
      Zhu, Zekun  and
      Jiang, Wei  and
      Zheng, Zhicheng  and
      Song, Jun  and
      Jiang, Yuning",
    booktitle = "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.acl-long.1487/",
    doi = "10.18653/v1/2026.acl-long.1487",
    pages = "32241--32255"
}
```