💪🧠 Reasoning Gym

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 and Countdown, have many correct solutions. To support this, we provide a standard interface for procedurally verifying solutions.

🖼️ Dataset Gallery

In 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 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.

✨ Example Usage

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.

Evaluation results of different reasoning models will be tracked in the reasoning-gym-eval repo.

👷 Contributing

Please see 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.