README.md

---
license: apache-2.0
library_name: transformers
pipeline_tag: image-text-to-text
base_model: Qwen/Qwen2.5-VL-7B-Instruct
tags:
- visual-reasoning
- tool-use
- iterative-reasoning
- grpo
---

<div align="center">
  <img src="https://huggingface.co/AdaReasoner/AdaReasoner-TC-7B-Randomized/resolve/main/docs/logo.png" alt="Logo" width="300">
  <h1 align="center">Dynamic Tool Orchestration for Iterative Visual Reasoning</h1>

  <a href="https://huggingface.co/papers/2601.18631">
    <img src="https://img.shields.io/badge/Paper-A42C25?style=for-the-badge&logo=arxiv&logoColor=white" alt="Paper">
  </a>
  <a href="https://github.com/ssmisya/AdaReasoner/tree/main/docs">
    <img src="https://img.shields.io/badge/Docs-1f6feb?style=for-the-badge&logo=readthedocs&logoColor=white" alt="Docs">
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  <a href="https://huggingface.co/collections/hitsmy/adareasoner">
    <img src="https://img.shields.io/badge/Data%20%26%20Model-fcd022?style=for-the-badge&logo=huggingface&logoColor=000" alt="Data & Model">
  </a>
  <a href="https://adareasoner.github.io">
    <img src="https://img.shields.io/badge/Homepage-2ea44f?style=for-the-badge&logo=googlechrome&logoColor=white" alt="Homepage">
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  <a href="https://github.com/ssmisya/AdaReasoner/tree/main/tool_server/tf_eval/demo">
  <img src="https://img.shields.io/badge/Demo-FF7C00?style=for-the-badge&logo=gradio&logoColor=white" alt="Demo">
  </a>
  <a href="https://www.youtube.com/watch?v=AtBoJYW_yDA">
    <img src="https://img.shields.io/badge/Video-FF0000?style=for-the-badge&logo=youtube&logoColor=white" alt="Video">
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This repository contains **AdaReasoner-TC-7B-Randomized**, a variant of the model presented in [AdaReasoner: Dynamic Tool Orchestration for Iterative Visual Reasoning](https://huggingface.co/papers/2601.18631).

## 🔔 Important Note on Model Status

The models released on this page belong to the AdaReasoner-TC series and are not the final RL-fine-tuned models.
They are trained using Tool Cold Start (TC) supervised fine-tuning only, and are intended for analysis, ablation, and reproducibility purposes.

For RL fine-tuned version, please refer to [Data & models](https://github.com/ssmisya/AdaReasoner/tree/main/docs/data_models.md)

## 📋 Model Description

**AdaReasoner-7B** is a vision-language model trained with dynamic tool orchestration capabilities for iterative visual reasoning. 

**AdaReasoner-TC** series are trained through TC (Tool Cold Start) supervised fine-tuning only, without subsequent RL fine-tuning.

This specific variant, **AdaReasoner-TC-7B-Randomized**, is trained with the *adaptive learning* method, enabling strong generalization to **unseen tools and tasks**. It is designed for open-ended and evolving tool environments where adaptability is required.

**Key Differences between TC variants:**
- **Randomized**: Trained with adaptive learning method, enabling zero-shot generalization to novel tools and task configurations.
- **Non-Randomized**: Trained without adaptive learning, offering more predictable behavior on familiar tools but lacking generalization.

## 📊 Performance

Please refer to our paper for detailed benchmark results across multiple visual reasoning tasks. AdaReasoner improves the 7B base model by +24.9% on average and surpasses strong proprietary systems such as GPT-5 on multiple tasks, including VSP and Jigsaw.

## 📚 Citation

If you use this model 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

Apache 2.0

## 🤝 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).