README.md

---
task_categories:
- multiple-choice
- question-answering
- visual-question-answering
language:
- en
size_categories:
- 1K<n<10K
configs:
- config_name: testmini
  data_files:
  - split: testmini
    path: testmini.parquet
dataset_info:
- config_name: testmini
  features:
  - name: sample_index
    dtype: string
  - name: problem_index
    dtype: string
  - name: problem_version
    dtype: string
  - name: question
    dtype: string
  - name: image
    dtype: image
  - name: answer
    dtype: string
  - name: question_type
    dtype: string
  - name: metadata
    struct:
    - name: split
      dtype: string
    - name: source
      dtype: string
    - name: subject
      dtype: string
    - name: subfield
      dtype: string
  - name: query_wo
    dtype: string
  - name: query_cot
    dtype: string
  - name: question_for_eval
    dtype: string
  - name: passrate_for_qwen2.5_vl_7b
    dtype: float64
  - name: difficulty_level_for_qwen2.5_vl_7b
    dtype: int64
  splits:
  - name: testmini
    num_bytes: 166789963
    num_examples: 3940
license: mit
---
# MathVerse with difficulty level tags

This dataset extends the 🤗 [MathVerse testmini](https://huggingface.co/datasets/AI4Math/MathVerse) 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/MathVerse_with_difficulty_level")
print(dataset)
```

## 📑 Citation

If you find this benchmark useful in your research, please consider citing this BibTex:

```latex
@inproceedings{zhang2024mathverse,
  title={MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?},
  author={Renrui Zhang, Dongzhi Jiang, Yichi Zhang, Haokun Lin, Ziyu Guo, Pengshuo Qiu, Aojun Zhou, Pan Lu, Kai-Wei Chang, Peng Gao, Hongsheng Li},
  booktitle={arXiv},
  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}, 
}
```