---
id: reasoning-gym
version: "0.1.25"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# reasoning-gym — A library of procedural dataset generators for training reasoning models

License: permissive · Maintenance: active · Downloads: 345.0K/mo

## What it is and what it does

Reasoning Gym is a Python library that generates procedural datasets and verifiable reasoning environments for training reinforcement learning models. It provides more than 100 tasks spanning algebra, arithmetic, computation, cognition, geometry, graph theory, logic, and games—each with adjustable complexity and built-in algorithmic verification of solutions. Some tasks have single correct answers; others like Rubik's Cube or Countdown have multiple valid solutions. The library generates virtually infinite training data on demand via a standard procedural interface, making it suitable for large-scale RL training without pre-generated dataset bottlenecks.

The package is designed for researchers and practitioners building reasoning models. It integrates with RL training frameworks (particularly the verifiers library) and supports both single-task and composite multi-task dataset creation with configurable weightings. Each dataset entry includes a question, answer, and metadata; scoring functions enable reward computation during training. The library is actively maintained, recently released, and already adopted by multiple research organizations including NVIDIA, Meta, and others for production reasoning model training.

Use it for:

- Generate infinite training data for reasoning model RL fine-tuning with procedurally controlled difficulty
- Benchmark and evaluate reasoning model performance across diverse task domains with algorithmic verification
- Create composite datasets combining multiple reasoning tasks with custom weightings for curriculum learning
- Build verifiable reward signals for RL training by calling task-specific scoring functions on model outputs
- Prototype new reasoning environments by extending the library's task generators for custom domains

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Reasoning Gym generates procedurally verifiable reasoning datasets and RL training environments across algebra, arithmetic, logic, games, and other domains, with adjustable complexity and algorithmic answer verification.

Yes. Reasoning Gym is actively maintained, has no known vulnerabilities, carries a permissive Apache-2.0 license, and requires only Python >= 3.10 with low install friction. It is purpose-built for RL reasoning model training and already adopted by major research labs. Install if you are training reasoning models or need procedurally verifiable reasoning datasets; skip if you need pre-generated static benchmarks or do not work with RL.

## Install

pip install reasoning-gym
uv add reasoning-gym
poetry add reasoning-gym

## Installing reasoning-gym

Before you install: Low install friction with a pure-Python wheel and 11 runtime dependencies. Active maintenance with a recent release (2026-03-28) and 1485 repository stars. Requires Python >= 3.10.

License in practice: Licensed under Apache-2.0 (permissive), allowing commercial use, modification, and distribution with minimal restrictions.

Quickstart:

pip install reasoning-gym

import reasoning_gym
data = reasoning_gym.create_dataset('leg_counting', size=10, seed=42)
for entry in data:
    score = data.score_answer(answer=entry['answer'], entry=entry)
    print(f"Question: {entry['question']}, Score: {score}")

Requires Python >= 3.10

Verify before relying:

- Whether all 100+ tasks are equally well-maintained or if some are experimental/unstable
- Performance characteristics and scalability limits for large dataset sizes
- Compatibility with specific RL frameworks beyond the mentioned verifiers library

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 345.0K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

procedural dataset generation, reasoning model training data, reinforcement learning environments, verifiable reasoning tasks, algorithmic problem generators, RL training dataset library, reasoning benchmark tasks, reinforcement-learning, dataset-generation, reasoning-benchmark

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