README.md

metadata

dataset_info:
  features:
    - name: problem
      dtype: string
    - name: level
      dtype: string
    - name: type
      dtype: string
    - name: solution
      dtype: string
    - name: solution_variants
      list: string
  splits:
    - name: train
      num_bytes: 6120153
      num_examples: 7500
  download_size: 3239633
  dataset_size: 6120153
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
language:
  - en
tags:
  - math
  - reinforcement-learning

MATH with Solution Variants

Dataset Summary

This dataset is a processed version of EleutherAI/hendrycks_math, which is derived from the original MATH dataset by Hendrycks et al.

The key addition in this version is the solution_variants column. This column contains semantically equivalent variations of the ground truth answer (e.g., "0.5" vs "1/2"), generated to serve as a robust reward signal for Reinforcement Learning (RL) training.

Dataset Structure

Each example contains:

• problem: The original mathematics problem statement.
• solution: The original step-by-step solution.
• solution_variants (New): A list of valid, equivalent short answers extracted and verified with math_verify.

Sample

{
  "problem": "What is 1/4 + 1/4?",
  "solution": "... \\boxed{1/2}",
  "solution_variants": ["1/2", "0.5"]
}

Citation & Attribution

If you use this dataset, please cite the original MATH paper:

@article{hendrycksmath2021,
  title={Measuring Mathematical Problem Solving With the MATH Dataset},
  author={Dan Hendrycks and Collin Burns and Saurav Kadavath and Akul Arora and Steven Basart and Eric Tang and Dawn Song and Jacob Steinhardt},
  journal={NeurIPS},
  year={2021}
}