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A dataset of challenging math problems designed to evaluate mathematical reasoning and solution steps of language models.
DESCRIPTION
The MATH dataset contains competition-style problems with step-by-step solutions across algebra, geometry, number theory, and more. It stresses multi-step reasoning and precise final answers. Chain-of-thought prompting and verifier models are often used for better performance. MATH is widely used to assess reasoning progress and fine-tuning strategies. It complements easier datasets like GSM8K by providing higher difficulty.
ALTERNATIVE DEFINITIONS
arXiv
We introduce the MATH dataset, consisting of challenging competition mathematics problems with step-by-step solutions to evaluate mathematical reasoning in machine learning models.
Emphasizes competition-level difficulty and step-by-step solutions.
MATH Dataset
The MATH dataset provides thousands of competition-level math problems and solutions for training and evaluating mathematical reasoning in models.
Stresses availability of problems and solutions for both training and evaluation.
RELATED TERMS
TheoremQA ProofWriter Chain-of-Thought Reasoning Benchmarking Mathematical Reasoning Step-by-Step Solutions Problem Solving
COMPETING TERMS
RELATIONS
- Broader: Dataset
KNOWLEDGE AREAS
AI Mathematics Data Science Testing & Validation MLOps
TARGET AUDIENCE
Data Scientist AI Engineer MLOps Engineer AI Product Manager Chief AI Officer
EXTERNAL SOURCES
- arXiv - Original MATH dataset paper
- MATH Dataset - Dataset repository and structure
- Papers with Code - Dataset overview and leaderboards
TOP QUESTIONS
How is the MATH dataset different from GSM8K?
MATH problems are generally harder and competition-style, requiring deeper reasoning than the grade-school arithmetic focus of GSM8K.
Category: Comparison
What capabilities does MATH test?
Symbolic manipulation, multi-step reasoning, and precision in final answers across diverse mathematical domains.
Category: Explanation
How do models typically improve on MATH?
Chain-of-thought prompting, specialized fine-tuning, and verification or self-consistency strategies boost accuracy.
Category: BestPractice
Is MATH suitable for training as well as evaluation?
Yes, many use it for supervised fine-tuning, but hold out subsets to preserve evaluation integrity.
Category: HowTo
What are common mistakes when using MATH?
Data leakage between train/test splits and over-reliance on few-shot prompts without verifiers.
Category: Mistakes
What is a risk when reporting MATH results?
Inconsistent solution parsing can inflate or deflate scores; use standardized answer extraction and validators.
Category: Risk
Does performance on MATH transfer to real-world tasks?
It indicates strong reasoning but may not reflect domain-specific constraints; validate on relevant problems.
Category: Misunderstanding
What’s a hot topic around MATH today?
Verifier models and program-aided reasoning approaches to reduce hallucinations and arithmetic slips.
Category: Topic
How often should I retest models on MATH?
When architectures, training data, or prompting strategies change; document versions for comparability.
Category: Process