task_categories:text-generation language:en license:apache-2.0 size_categories:10K<n<100K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars region:us math aimo
---
language:
- en
license: apache-2.0
task_categories:
- text-generation
pretty_name: NuminaMath TIR
dataset_info:
  features:
  - name: problem
    dtype: string
  - name: solution
    dtype: string
  - name: messages
    list:
    - name: content
      dtype: string
    - name: role
      dtype: string
  splits:
  - name: train
    num_bytes: 327147067
    num_examples: 72441
  - name: test
    num_bytes: 461331
    num_examples: 99
  download_size: 147557990
  dataset_size: 327608398
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
tags:
- math
- aimo
---

# Dataset Card for NuminaMath CoT

## Dataset Description

- **Homepage:** https://projectnumina.ai
- **Repository:** https://github.com/project-numina/aimo-progress-prize
- **Paper:** https://github.com/project-numina/aimo-progress-prize/blob/main/report/numina_dataset.pdf
- **Leaderboard:** 
- **Point of Contact:** [Jia Li](jia@projectnumina.ai)


### Dataset Summary

Tool-integrated reasoning (TIR) plays a crucial role in this competition. However, collecting and annotating such data is both costly and time-consuming. To address this, we selected approximately 70k problems from the NuminaMath-CoT dataset, focusing on those with numerical outputs, most of which are integers. We then utilized a pipeline leveraging GPT-4 to generate TORA-like reasoning paths, executing the code and producing results until the solution was complete. We filtered out solutions where the final answer did not match the reference and repeated this process three times to ensure accuracy and consistency. This iterative approach allowed us to generate high-quality TORA data efficiently.

### Licensing Information

The dataset is available under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0).

### Citation Information

```
@misc{numina_math_datasets,
  author = {Jia LI, Edward Beeching, Lewis Tunstall, Ben Lipkin, Roman Soletskyi, Shengyi Costa Huang, Kashif Rasul, Longhui Yu, Albert Jiang, Ziju Shen, Zihan Qin, Bin Dong, Li Zhou, Yann Fleureau, Guillaume Lample, and Stanislas Polu},
  title = {NuminaMath TIR},
  year = {2024},
  publisher = {Numina},
  journal = {Hugging Face repository},
  howpublished = {\url{[https://huggingface.co/AI-MO/NuminaMath-TIR](https://github.com/project-numina/aimo-progress-prize/blob/main/report/numina_dataset.pdf)}}
}
```