For the image corpus, you can directly download M-BEIR. To embed and index it, you can follow the repository
For the table corpus, you can download, embed and index Open-WikiTable following the repository, or you can download directly the one we have already preprocessed from here.
Retrievers Preparation
For the Text-Image Retriever, you can directly download UniIR
For the Table Retriever, you can train it with the help of repository, or you can download it directly from here.
Datasets
We have prepared all the text datasets in ./datasets, for images you need to download them from:
InfoSeek: InfoSeek images can be downloaded from OVEN
Dyn-VQA: Dynamic VQA images can be downloaded from DynVQA_en.202412
WebQA: WebQA images can be downloaded from Google Drive
Training
If you do not want to train the model, you can download R1-Router and skip this section to Evaluation
Data Synthesis
If you want to use the ready-to-use synthetic data directly, you can skip this section to Step-GRPO Training
First, we need to synthesis the data step by step:
bash src/data_synthesis/data_synthesis.sh
Step-GRPO Training
Our training framework is based on EasyR1. All you need to do is download it and replace some files with the files in ./Easy-R1.
Then start training with the command:
We appreciate your citations if you find our paper relevant and useful to your research!
@article{peng2025r1,
title={Learning to Route Queries across Knowledge Bases for Step-wise Retrieval-Augmented Reasoning},
author={Peng, Chunyi and Xu, Zhipeng and Liu, Zhenghao and Li, Yishan and Yan, Yukun and Wang, Shuo and Liu, Zhiyuan and Gu, Yu and Yu, Minghe and Yu, Ge and Sun, Maosong},
year={2025}
url={https://arxiv.org/abs/2505.22095},
}
Contact Us
If you have questions, suggestions, or bug reports, please email us. We will try our best to help you.
