Practical overview
When it fits
Use it when a Python-based reasoning workflow needs procedurally generated tasks and answer verification, either as dynamically generated training examples or as datasets prepared ahead of training.
Who it is for
- AI researchers building reinforcement-learning workflows for reasoning models
- Developers who need configurable reasoning datasets, task mixtures or algorithmic answer scoring
- Teams preparing reasoning examples for model evaluation or training pipelines
Documented uses
- Generate reasoning questions and answers with task metadata.
- Adjust task parameters and combine task types into weighted datasets.
- Score model responses algorithmically for use as training rewards.
- Prepare data dynamically during training or ahead of time for conversion to Hugging Face format.
- Build task collections spanning domains such as arithmetic, logic, geometry, graph theory and games.
Requirements & limits
The package requires Python 3.10 or later, is installable from PyPI with pip, and is licensed under Apache-2.0. The repository notes that the PyPI version may lag behind the main branch by a few days.
- The project provides dataset generation and scoring, while model training remains part of the user's own workflow.
- More than 100 tasks span domains including algebra, arithmetic, computation, cognition, geometry, graph theory, logic and games.
- Some tasks have multiple valid solutions; verification is provided through task-specific scoring interfaces.
- The optional
reasoning-gym[scoring]extra enables symbolic math verification for the cascade scorer. - The maintainers recommend the separate
verifierslibrary for a direct training integration; they also describe use with major RL frameworks.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install the library and generate a scored dataset
Install Reasoning Gym and create a small task dataset whose answers can be checked with its scoring method.
Before you start
- Python 3.10 or later
- pip
Steps
Install the package with the command below.
pip install reasoning-gymImport
reasoning_gymand create a dataset, for example withreasoning_gym.create_dataset('leg_counting', size=10, seed=42).Iterate over dataset entries to read each question, answer and metadata.
Pass an entry and its answer to
data.score_answer(answer=..., entry=...)to obtain the algorithmic score.
Expected outcome
A generated dataset of reasoning examples with a scoring method available to check answers.
Guide limitations
- The PyPI package may lag behind the repository's main branch by a few days.
- This procedure generates and scores examples; it does not train a model.
Prepare a composite task dataset
Configure a dataset that combines multiple task types with relative weights.
Before you start
- Reasoning Gym installed in a Python 3.10-or-later environment
- Task names and configuration values selected from the project dataset gallery
Steps
Create
DatasetSpecentries for the task types to include, setting each task's weight and configuration.Pass the specifications to
reasoning_gym.create_dataset('composite', size=..., seed=..., datasets=specs).Use the generated composite dataset as input to the training workflow.
Expected outcome
A generated dataset combining configured task types according to their relative weights.
Guide limitations
- Available task names and configurable parameters vary by task; consult the project gallery.
- The README documents dataset generation but does not prescribe a particular model-training command.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 15 October 2026
- Original website title
- open-thought/reasoning-gym · Official homepage title · retained in its source language