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README.md
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# SRPO Dataset: Reflection-Aware RL Training Data
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This repository provides the multimodal reasoning dataset used in the paper:
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**[SRPO: Enhancing Multimodal LLM Reasoning via Reflection-Aware Reinforcement Learning](https://arxiv.org/abs/2506.01713)**
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We release two versions of the dataset:
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- **39K version** (`modified_39Krelease.jsonl` + `images.zip`)
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- **Enhanced 47K+ version** (`47K_release_plus.jsonl` + `47K_release_plus.zip`)
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Both follow the same unified format, containing multimodal (image–text) reasoning data with self-reflection supervision. The 47K+ version further incorporates high-quality external datasets, such as [PhyX](https://arxiv.org/abs/2505.15929) and [We-Math 2.0](https://arxiv.org/abs/2508.10433), to strengthen physical and mathematical reasoning.
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## 📂 Data Format
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The data is stored in **JSON Lines (`.jsonl`)** format. Each sample includes an ID, a multimodal input (image + text), and the ground-truth answer.
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Example:
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```json
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{
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"id": "12",
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"message": "[{\"role\": \"user\", \"content\": [{\"type\": \"image\", \"image\": \"/path/to/images/Processed-65d5feaa-714b-4a86-97e4-dc72802c4593-0.jpg\"}, {\"type\": \"text\", \"text\": \"<image>\\nAre there more berries with two leaves or with one leaf?\"}]}]",
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"answer": "\\boxed{Two leaves}"
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}
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```
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- **id**: Unique sample identifier
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- **message**: Conversation-style user input, combining image reference and textual query
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- **answer**: Ground-truth answer in LaTeX-style format
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## 📂 Citation
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```
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@article{wan2025srpo,
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title={Srpo: Enhancing multimodal llm reasoning via reflection-aware reinforcement learning},
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author={Wan, Zhongwei and Dou, Zhihao and Liu, Che and Zhang, Yu and Cui, Dongfei and Zhao, Qinjian and Shen, Hui and Xiong, Jing and Xin, Yi and Jiang, Yifan and others},
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journal={arXiv preprint arXiv:2506.01713},
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year={2025}
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}
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@article{shen2025phyx,
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title={PhyX: Does Your Model Have the "Wits" for Physical Reasoning?},
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author={Shen, Hui and Wu, Taiqiang and Han, Qi and Hsieh, Yunta and Wang, Jizhou and Zhang, Yuyue and Cheng, Yuxin and Hao, Zijian and Ni, Yuansheng and Wang, Xin and others},
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journal={arXiv preprint arXiv:2505.15929},
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year={2025}
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}
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```
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