Reasoning Gym
## 🧠 About
**Reasoning Gym** is a community-created Python library of procedural dataset generators and algorithmically verifiable reasoning environments for training reasoning models with reinforcement learning (RL). The goal is to generate virtually infinite training data with adjustable complexity.
It currently provides **more than 100** tasks over many domains, including but not limited to _algebra_, _arithmetic_, _computation_, _cognition_, _geometry_, _graph theory_, _logic_, and many common _games_.
Some tasks have a single correct answer, while others, such as [Rubik‘s Cube](https://en.wikipedia.org/wiki/Rubik%27s_Cube) and [Countdown](), have many correct solutions. To support this, we provide a standard interface for procedurally verifying solutions.
## 🖼️ Dataset Gallery
In [GALLERY.md](https://github.com/open-thought/reasoning-gym/blob/main/GALLERY.md), you can find example outputs of all datasets available in `reasoning-gym`.
## ⬇️ Installation
The `reasoning-gym` package requires Python >= 3.11.
Install the latest published [package from PyPI](https://pypi.org/project/reasoning-gym/) via `pip`:
```
pip install reasoning-gym
```
_Note that this project is currently under active development, and the version published on PyPI may be a few days behind `main`._
## 🛠️ Development
For development setup, see [CONTRIBUTING.md](CONTRIBUTING.md#development-setup).
## ✨ Example Usage
```python
import reasoning_gym
data = reasoning_gym.create_dataset('leg_counting', size=10, seed=42)
for i, x in enumerate(data):
print(f'{i}: q="{x['question']}", a="{x['answer']}"')
print('metadata:', x['metadata'])
# use the dataset's `score_answer` method for algorithmic verification
assert data.score_answer(answer=x['answer'], entry=x) == 1.0
```
Output:
```
0: q="How many legs are there in total if you have 1 sea slug, 1 deer?", a="4"
metadata: {'animals': {'sea slug': 1, 'deer': 1}, 'total_legs': 4}
1: q="How many legs are there in total if you have 2 sheeps, 2 dogs?", a="16"
metadata: {'animals': {'sheep': 2, 'dog': 2}, 'total_legs': 16}
2: q="How many legs are there in total if you have 1 crab, 2 lobsters, 1 human, 1 cow, 1 bee?", a="42"
...
```
## 🔍 Evaluation
Instructions for running the evaluation scripts are provided in [eval/README.md](https://github.com/open-thought/reasoning-gym/blob/main/eval/README.md).
Evaluation results of different reasoning models will be tracked in the [reasoning-gym-eval](https://github.com/open-thought/reasoning-gym-eval) repo.
## 👷 Contributing
Please see [CONTRIBUTING.md](CONTRIBUTING.md).
If you have ideas for dataset generators please create an issue here or contact us in the `#reasoning-gym` channel of the [GPU-Mode discord server](https://discord.gg/gpumode).
[](https://discord.gg/gpumode)
## 🚀 Projects Using Reasoning Gym
Following is a list of awesome projects building on top of Reasoning Gym:
- [Verifiers: Reinforcement Learning with LLMs in Verifiable Environments](https://github.com/willccbb/verifiers)
- [(NVIDIA) ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models](https://arxiv.org/abs/2505.24864)
## 📝 Citation
If you use this library in your research, please cite the paper:
```bibtex
@misc{stojanovski2025reasoninggymreasoningenvironments,
title={REASONING GYM: Reasoning Environments for Reinforcement Learning with Verifiable Rewards},
author={Zafir Stojanovski and Oliver Stanley and Joe Sharratt and Richard Jones and Abdulhakeem Adefioye and Jean Kaddour and Andreas Köpf},
year={2025},
eprint={2505.24760},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2505.24760},
}
```