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---
license: mit
language:
- en
size_categories:
- n<1K
pretty_name: Adaptive Graph Pruning Training Dataset
dataset_info:
- config_name: coding
features:
- name: task
dtype: string
- name: edge_weight
sequence:
sequence: float32
- name: mask
sequence: float32
splits:
- name: train
num_bytes: 85430
num_examples: 140
- config_name: general_reasoning
features:
- name: task
dtype: string
- name: edge_weight
sequence:
sequence: float32
- name: mask
sequence: float32
splits:
- name: train
num_bytes: 97455
num_examples: 138
- config_name: math_reasoning
features:
- name: task
dtype: string
- name: edge_weight
sequence:
sequence: float32
- name: mask
sequence: float32
splits:
- name: train
num_bytes: 31794
num_examples: 94
configs:
- config_name: coding
data_files:
- split: train
path: coding/train.json
- config_name: general_reasoning
data_files:
- split: train
path: general_reasoning/train.json
- config_name: math_reasoning
data_files:
- split: train
path: math_reasoning/train.json
---
# Dataset Card for AGP-Training
## Dataset Description
- **Homepage:** https://resurgamm.github.io/AGP/
- **Repository:** https://github.com/Resurgamm/AGP
- **Paper:** https://arxiv.org/abs/2506.02951
### Dataset Summary
AGP-Training is the dataset for training Adaptive Graph Pruning (AGP). We open-source our training set so that you can reproduce our work.
For further usage, please refer to [our repository](https://github.com/Resurgamm/AGP)
### Citation
If you find AGP helpful in your research, please consider citing:
```bibtex
@article{li2025adaptive,
title={Adaptive Graph Pruning for Multi-Agent Communication},
author={Li, Boyi and Zhao, Zhonghan and Lee, Der-Horng and Wang, Gaoang},
journal={arXiv preprint arXiv:2506.02951},
year={2025}
}
``` |