Polaris-Dataset-53K
收藏魔搭社区2025-12-05 更新2025-06-28 收录
下载链接:
https://modelscope.cn/datasets/POLARIS-Project/Polaris-Dataset-53K
下载链接
链接失效反馈官方服务:
资源简介:
## Overview
Training dataset for Polaris Preview models. The dataset is filtered from [DeepScaleR-Preview-Dataset](https://huggingface.co/datasets/agentica-org/DeepScaleR-Preview-Dataset) and [AReal-boba-Data](https://huggingface.co/datasets/inclusionAI/AReaL-boba-Data)
## Format
Each row in the `jsonl` file contains:
- **problem**: The input problem.
- **answer**: The answer to the problem
- **difficulty**: The pass rate of the problem estimated by `Deepseek-R1-distill-Qwen-7B`
## Citation
```bibtex
@misc{Polaris2025,
title = {POLARIS: A Post-Training Recipe for Scaling Reinforcement Learning on Advanced Reasoning Models},
url = {https://hkunlp.github.io/blog/2025/Polaris},
author = {An, Chenxin and Xie, Zhihui and Li, Xiaonan and Li, Lei and Zhang, Jun and Gong, Shansan and Zhong, Ming and Xu, Jingjing and Qiu, Xipeng and Wang, Mingxuan and Kong, Lingpeng}
year = {2025}
}
```
## 概述
本数据集为Polaris Preview模型的训练数据集,从[DeepScaleR-Preview-Dataset](https://huggingface.co/datasets/agentica-org/DeepScaleR-Preview-Dataset)与[AReal-boba-Data](https://huggingface.co/datasets/inclusionAI/AReaL-boba-Data)中筛选得到。
## 数据格式
该`jsonl`文件的每一行均包含以下字段:
- **problem(问题)**:输入的待求解问题
- **answer(答案)**:该问题的对应解答
- **difficulty(难度)**:由`Deepseek-R1-distill-Qwen-7B`模型估算得到的该问题的通过率
## 引用
bibtex
@misc{Polaris2025,
title = {POLARIS: A Post-Training Recipe for Scaling Reinforcement Learning on Advanced Reasoning Models},
url = {https://hkunlp.github.io/blog/2025/Polaris},
author = {An, Chenxin and Xie, Zhihui and Li, Xiaonan and Li, Lei and Zhang, Jun and Gong, Shansan and Zhong, Ming and Xu, Jingjing and Qiu, Xipeng and Wang, Mingxuan and Kong, Lingpeng}
year = {2025}
}
提供机构:
maas
创建时间:
2025-06-23



