Datasets:

FanqingM
/

MMK12

   - split: test
     path: data/test-*
 ---
+# MMK12
+[\[📂 GitHub\]](https://github.com/ModalMinds/MM-EUREKA)  [\[📜 Paper\]](https://arxiv.org/abs/2503.07365v2)
+***`2025/04/16:` We release a new version of MMK12, which can greatly enhance the multimodal reasoning of Qwen-2.5-VL.***
+We use MMK12 for RL training to develop MM-EUREKA-7B and MM-EUREKA-32B, with specific training details available in [paper](https://arxiv.org/abs/2503.07365v2).
+Both models demonstrate excellent performance on the MMK12 evaluation set (a multidisciplinary multimodal reasoning benchmark), with MM-EUREKA-32B ranking second only to o1.
+| Model | Mathematics | Physics | Chemistry | Biology | Avg. |
+|-------|-------------|---------|-----------|---------|------|
+| **Closed-Source Models** |
+| Claude3.7-Sonnet | 57.4 | 53.4 | 55.4 | 55.0 | 55.3 |
+| GPT-4o | 55.8 | 41.2 | 47.0 | 55.4 | 49.9 |
+| o1 | 81.6 | 68.8 | 71.4 | 74.0 | 73.9 |
+| Gemini2-flash | 76.8 | 53.6 | 64.6 | 66.0 | 65.2 |
+| **Open-Source General Models** |
+| InternVL2.5-VL-8B | 46.8 | 35.0 | 50.0 | 50.8 | 45.6 |
+| Qwen-2.5-VL-7B | 58.4 | 45.4 | 56.4 | 54.0 | 53.6 |
+| InternVL2.5-VL-38B | 61.6 | 49.8 | 60.4 | 60.0 | 58.0 |
+| Qwen-2.5-VL-32B | 71.6 | 59.4 | 69.6 | 66.6 | 66.8 |
+| InternVL2.5-VL-78B | 59.8 | 53.2 | 68.0 | 65.2 | 61.6 |
+| Qwen-2.5-VL-72B | 75.6 | 64.8 | 69.6 | 72.0 | 70.5 |
+| **Open-Source Reasoning Models** |
+| InternVL2.5-8B-MPO | 26.6 | 25.0 | 42.4 | 44.0 | 34.5 |
+| InternVL2.5-38B-MPO | 41.4 | 42.8 | 55.8 | 53.2 | 48.3 |
+| QVQ-72B-Preview | 61.4 | 57.4 | 62.6 | 64.4 | 61.5 |
+| Adora | 63.6 | 50.6 | 59.0 | 59.0 | 58.1 |
+| R1-Onevision | 44.8 | 33.8 | 39.8 | 40.8 | 39.8 |
+| OpenVLThinker-7 | 63.0 | 53.8 | 60.6 | 65.0 | 60.6 |
+| **Ours** |
+| MM-Eureka-7B | 71.2 | 56.2 | 65.2 | 65.2 | 64.5 |
+| MM-Eureka-32B | 74.6 | 62.0 | 75.4 | 76.8 | 72.2 |
+## Data fields
+| Key        | Description                         |
+| ---------- | ----------------------------------- |
+| `id`    | ID.                         |
+| `subject`    | subject: math, physics, chemistry, and biology                         |
+| `image`    | Image path.                         |
+| `question` | Input query.                        |
+| `answer`   | Verified Answer.   |
+## Citation
+If you find this project useful in your research, please consider citing:
+```BibTeX
+@article{meng2025mm,
+  title={MM-Eureka: Exploring Visual Aha Moment with Rule-based Large-scale Reinforcement Learning},
+  author={Meng, Fanqing and Du, Lingxiao and Liu, Zongkai and Zhou, Zhixiang and Lu, Quanfeng and Fu, Daocheng and Shi, Botian and Wang, Wenhai and He, Junjun and Zhang, Kaipeng and others},
+  journal={arXiv preprint arXiv:2503.07365},
+  year={2025}
+}