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dataset_info:
features:
- name: data_source
dtype: string
- name: prompt
list:
- name: content
dtype: string
- name: role
dtype: string
- name: images
list:
- name: bytes
dtype: binary
- name: ability
dtype: string
- name: env_name
dtype: string
- name: reward_model
struct:
- name: ground_truth
dtype: string
- name: style
dtype: string
- name: extra_info
struct:
- name: answer
dtype: string
- name: index
dtype: string
- name: question
dtype: string
- name: split
dtype: string
- name: randomized_to_original
dtype: string
splits:
- name: train
num_bytes: 1971977439
num_examples: 15000
- name: validation
num_bytes: 74206529
num_examples: 500
download_size: 1931231003
dataset_size: 2046183968
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
task_categories:
- image-text-to-text
license: apache-2.0
language:
- en
tags:
- visual-reasoning
- tool-use
- multimodal
- mllm
---
# AdaReasoner Dataset
[**Project Page**](https://adareasoner.github.io/) | [**Paper**](https://huggingface.co/papers/2601.18631) | [**GitHub**](https://github.com/ssmisya/AdaReasoner)
AdaReasoner is a family of multimodal models that learn tool use as a general reasoning skill rather than as tool-specific behavior. This dataset provides the training and evaluation data used to enable long-horizon, multi-step tool interactions. It was curated to help models infer tool utility from task context and intermediate outcomes, enabling the coordination of multiple tools and generalization to unseen ones.
## 🧩 Data Format
The data is stored in Parquet format. According to the official documentation, each item in the dataset typically follows this structure:
```python
prompt = [
{
"content": system_prompt,
"role": "system"
},
{
"content": f"{question_text}",
"role": "user"
}
]
item = {
"data_source": "jigsaw_coco",
"prompt": prompt,
"images": [{"bytes": question_image_bytes}] + choice_images,
"ability": "visual_reasoning",
"env_name": "jigsaw",
"reward_model": {
"ground_truth": correct_letter.lower(),
"style": "model"
},
"extra_info": { # Used for reward calculation
"extra_info1": "...",
}
}
```
## 📚 Citation
If you use this dataset in your research, please cite:
```bibtex
@article{song2026adareasoner,
title={AdaReasoner: Dynamic Tool Orchestration for Iterative Visual Reasoning},
author={Song, Mingyang and Sun, Haoyu and Gu, Jiawei and Li, Linjie and Xu, Luxin and Krishna, Ranjay and Cheng, Yu},
journal={arXiv preprint arXiv:2601.18631},
year={2026}
}
```
## 📄 License
This dataset is licensed under the Apache 2.0 License.
## 🤝 Acknowledgments
This model is part of the AdaReasoner project. For more information, visit our [GitHub repository](https://github.com/ssmisya/AdaReasoner).
## 📧 Contact
For questions and feedback, please open an issue in our [GitHub repository](https://github.com/ssmisya/AdaReasoner). |