Big-Math Reinforcement Learning Mathematics Dataset

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Big-Math is a large-scale, high-quality mathematical dataset designed for the application of reinforcement learning (RL) in language models. The dataset was released by researchers from Stanford University and SynthLabs in 2025. The related paper is “Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language Models".

Dataset characteristics

Big-Math contains over 250k high-quality math problems, each with verifiable answers. The problems in the dataset meet 3 key criteria:

• The only verifiable solution: Each question has only one correct answer.
• Closed-form solution: Questions have clear solutions. Each question comes with a verifiable answer.
• Open-ended questions: The problem statement is open, allowing multiple solutions.