metadata

language:
  - en
tags:
  - multimodal
  - reinforcement-learning
  - reflection
  - reasoning
  - dataset
license: mit
task_categories:
  - question-answering
pretty_name: SRPO Dataset
size_categories:
  - 10K<n<100K

SRPO Dataset: Reflection-Aware RL Training Data

This repository provides the multimodal reasoning dataset used in the paper:

SRPO: Enhancing Multimodal LLM Reasoning via Reflection-Aware Reinforcement Learning

We release two versions of the dataset:

39K version (modified_39Krelease.jsonl + images.zip)
Enhanced 47K+ version (47K_release_plus.jsonl + 47K_release_plus.zip)

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 and We-Math 2.0, to strengthen physical and mathematical reasoning.

📂 Data Format

The data is stored in JSON Lines (.jsonl) format. Each sample includes an ID, a multimodal input (image + text), and the ground-truth answer.

Example:

{
  "id": "12",
  "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?\"}]}]",
  "answer": "\\boxed{Two leaves}"
}

id: Unique sample identifier
message: Conversation-style user input, combining image reference and textual query
answer: Ground-truth answer in LaTeX-style format

📂 Citation

@article{wan2025srpo,
  title={Srpo: Enhancing multimodal llm reasoning via reflection-aware reinforcement learning},
  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},
  journal={arXiv preprint arXiv:2506.01713},
  year={2025}
}

@article{shen2025phyx,
  title={PhyX: Does Your Model Have the "Wits" for Physical Reasoning?},
  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},
  journal={arXiv preprint arXiv:2505.15929},
  year={2025}
}