README.md

---
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).