We-Math2.0-Standard

# Dataset Card for We-Math 2.0

[GitHub](https://github.com/We-Math/We-Math2.0) | [Paper](https://arxiv.org/abs/2508.10433) | [Website](https://we-math2.github.io/)

We-Math 2.0 is a unified system designed to comprehensively enhance the mathematical reasoning capabilities of Multimodal Large Language Models (MLLMs).
It integrates a structured mathematical knowledge system, model-centric data space modeling, and a reinforcement learning (RL)-based training paradigm to achieve both broad conceptual coverage and robust reasoning performance across varying difficulty levels.

The key contributions of We-Math 2.0 are fourfold:

- MathBook Knowledge System — A five-level hierarchical structure encompassing 491 knowledge points and 1,819 fundamental principles.
- MathBook-Standard & MathBook-Pro — MathBook-Standard ensures wide conceptual coverage and flexibility via dual expansion, while MathBook-Pro defines a three-dimensional difficulty space and generates 7 progressive variants per problem for robust training.
- MathBook-RL — A two-stage RL framework comprising Cold-Start Fine-tuning for knowledge-oriented chain-of-thought alignment, and Progressive Alignment RL with average-reward learning and dynamic data scheduling for gradual alignment across difficulty levels.
- MathBookEval — A comprehensive benchmark covering all 491 knowledge points with diverse reasoning step distributions.

## Citation
If you find the content of this project helpful, please cite our paper as follows:

```
@misc{qiao2025wemath20versatilemathbook,
title={We-Math 2.0: A Versatile MathBook System for Incentivizing Visual Mathematical Reasoning},
author={Runqi Qiao and Qiuna Tan and Peiqing Yang and Yanzi Wang and Xiaowan Wang and Enhui Wan and Sitong Zhou and Guanting Dong and Yuchen Zeng and Yida Xu and Jie Wang and Chong Sun and Chen Li and Honggang Zhang},
year={2025},
eprint={2508.10433},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2508.10433},
}
```

# 数据集卡片：We-Math 2.0
[GitHub](https://github.com/We-Math/We-Math2.0) | [Paper](https://arxiv.org/abs/2508.10433) | [Website](https://we-math2.github.io/)

We-Math 2.0 是一款旨在全面提升多模态大语言模型（Multimodal Large Language Models, MLLMs）数学推理能力的统一系统。该系统整合了结构化数学知识体系、以模型为中心的数据空间建模，以及基于强化学习（Reinforcement Learning, RL）的训练范式，可实现广泛的概念覆盖，并在不同难度层级下均具备稳健的推理性能。

We-Math 2.0 的核心贡献分为四个方面：
- **MathBook 知识体系**：采用五级层级结构，涵盖491个知识点与1819条基础原理。
- **MathBook-Standard 与 MathBook-Pro**：MathBook-Standard 通过双重扩展实现广泛的概念覆盖与灵活性；MathBook-Pro 则定义了三维难度空间，并为每道题目生成7种渐进式变体，以支撑稳健训练。
- **MathBook-RL**：采用两阶段强化学习框架，包含面向知识导向思维链对齐的冷启动微调，以及结合平均奖励学习与动态数据调度的渐进对齐强化学习，以实现跨难度层级的逐步对齐。
- **MathBookEval**：覆盖全部491个知识点的综合基准测试集，包含多样化的推理步骤分布。

## 引用
若您认为本项目内容对您有所帮助，请按以下格式引用我们的论文：

@misc{qiao2025wemath20versatilemathbook,
title={We-Math 2.0: A Versatile MathBook System for Incentivizing Visual Mathematical Reasoning},
author={Runqi Qiao and Qiuna Tan and Peiqing Yang and Yanzi Wang and Xiaowan Wang and Enhui Wan and Sitong Zhou and Guanting Dong and Yuchen Zeng and Yida Xu and Jie Wang and Chong Sun and Chen Li and Honggang Zhang},
year={2025},
eprint={2508.10433},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2508.10433},
}