---
license: cc-by-4.0
language:
- en
task_categories:
- text-generation
tags:
- reasoning
- math
- code
- science
- supervised-fine-tuning
- synthetic
pretty_name: NVIDIA Open Reasoning Balanced 100k
size_categories:
- 10K<n<100K
---

# nvidia_open_reasoning_balanced_100k

A domain-balanced 100k reasoning SFT dataset built from three NVIDIA Open Reasoning
datasets: **33,333 examples each** for math, code, and science (99,999 total).

Each example is a single-turn conversation with a full reasoning trace:

```
conversations: [
  {"from": "human", "value": "<problem>"},
  {"from": "gpt",   "value": "<think>\n<reasoning trace>\n</think><final solution>"}
]
```

## Columns

| column | description |
|---|---|
| `conversations` | human / gpt turns (gpt = `<think>...</think>` reasoning + solution) |
| `domain` | `math` / `code` / `science` (33,333 each) |
| `source` | origin dataset and subset, e.g. `nvidia/OpenMathReasoning\|aops_c6_high_school_olympiads`, `nvidia/OpenCodeReasoning\|codeforces` |
| `difficulty` | difficulty label where the origin dataset provides one (`None` otherwise) |

## Source datasets

This dataset is a balanced sample of the following NVIDIA datasets. All three are
released under CC BY 4.0, and this dataset inherits that license.

| domain | dataset | rows sampled |
|---|---|---|
| math | [nvidia/OpenMathReasoning](https://huggingface.co/datasets/nvidia/OpenMathReasoning) | 33,333 |
| code | [nvidia/OpenCodeReasoning](https://huggingface.co/datasets/nvidia/OpenCodeReasoning) | 33,333 |
| science | [nvidia/OpenScienceReasoning-2](https://huggingface.co/datasets/nvidia/OpenScienceReasoning-2) | 33,333 |

## Citation

This repository is only a convenience re-packaging. **Please cite the original
NVIDIA datasets**, not this repository:

```bibtex
@article{moshkov2025aimo2,
  title   = {AIMO-2 Winning Solution: Building State-of-the-Art Mathematical Reasoning Models with OpenMathReasoning dataset},
  author  = {Ivan Moshkov and Darragh Hanley and Ivan Sorokin and Shubham Toshniwal and Christof Henkel and Benedikt Schifferer and Wei Du and Igor Gitman},
  year    = {2025},
  journal = {arXiv preprint arXiv:2504.16891}
}

@article{ahmad2025opencodereasoning,
  title   = {OpenCodeReasoning: Advancing Data Distillation for Competitive Coding},
  author  = {Wasi Uddin Ahmad and Sean Narenthiran and Somshubra Majumdar and Aleksander Ficek and Siddhartha Jain and Jocelyn Huang and Vahid Noroozi and Boris Ginsburg},
  year    = {2025},
  eprint  = {2504.01943},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  url     = {https://arxiv.org/abs/2504.01943}
}
```

For the science subset, cite the dataset page directly:

```bibtex
@misc{openscience_reasoning_2,
  title  = {OpenScienceReasoning-2},
  author = {NVIDIA},
  year   = {2025},
  howpublished = {\url{https://huggingface.co/datasets/nvidia/OpenScienceReasoning-2}}
}
```