README.md

metadata

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 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.

🚀 Data Usage

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:

@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}, 
}