README.md

---
task_categories:
- multiple-choice
- question-answering
- visual-question-answering
language:
- en
size_categories:
- 1K<n<10K
configs:
- config_name: val
  data_files:
  - split: val
    path: "mmstar.parquet"
dataset_info:
  - config_name: val
    features:
      - name: index
        dtype: int64
      - name: question
        dtype: string
      - name: image
        dtype: image
      - name: answer
        dtype: string
      - name: category
        dtype: string
      - name: l2_category
        dtype: string
      - name: meta_info
        struct:
        - name: source
          dtype: string
        - name: split
          dtype: string
        - name: image_path
          dtype: string
        - name: passrate_for_qwen2.5_vl_7b
          dtype: float64
        - name: difficulty_level_for_qwen2.5_vl_7b
          dtype: int64
    splits:
      - name: val
        num_bytes: 44831593
        num_examples: 1500
---


# MMStar with difficulty level tags

This dataset extends the 🤗 [MMStar](https://huggingface.co/datasets/Lin-Chen/MMStar) benchmark by introducing two additional tags: **passrate_for_qwen2.5_vl_7b** and **difficulty_level_for_qwen2.5_vl_7b**. Further details are available in our paper [The Synergy Dilemma of Long-CoT SFT and RL: Investigating Post-Training Techniques for Reasoning VLMs](https://www.arxiv.org/pdf/2507.07562).



## 🚀 Data Usage


```python
from datasets import load_dataset

dataset = load_dataset("JierunChen/MMStar_with_difficulty_level")
print(dataset)
```

## 📑 Citation

If you find this benchmark useful in your research, please consider citing this BibTex:
```latex
@article{chen2024we,
  title={Are We on the Right Way for Evaluating Large Vision-Language Models?},
  author={Chen, Lin and Li, Jinsong and Dong, Xiaoyi and Zhang, Pan and Zang, Yuhang and Chen, Zehui and Duan, Haodong and Wang, Jiaqi and Qiao, Yu and Lin, Dahua and others},
  journal={arXiv preprint arXiv:2403.20330},
  year={2024}
}

@misc{chen2025synergydilemmalongcotsft,
      title={The Synergy Dilemma of Long-CoT SFT and RL: Investigating Post-Training Techniques for Reasoning VLMs}, 
      author={Jierun Chen and Tiezheng Yu and Haoli Bai and Lewei Yao and Jiannan Wu and Kaican Li and Fei Mi and Chaofan Tao and Lei Zhu and Manyi Zhang and Xiaohui Li and Lu Hou and Lifeng Shang and Qun Liu},
      year={2025},
      eprint={2507.07562},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2507.07562}, 
}
```