MATH

Aggregates 12,500 hand-curated competition mathematics problems sourced from AMC (American Mathematics Competitions), AIME (American Invitational Mathematics Examination), and other prestigious math olympiads. Problems are structured with metadata including difficulty ratings (1-5 scale), subject classification across 7 domains, and complete step-by-step solutions. The curation process filters for problems that require genuine mathematical reasoning rather than pattern matching, enabling reliable evaluation of model reasoning depth.

Enables selective sampling of problems across a 5-level difficulty scale, allowing researchers to construct evaluation sets tailored to specific model capability ranges. The difficulty metadata is pre-assigned during curation, enabling efficient filtering without re-evaluation. This supports progressive evaluation strategies where models are first tested on easier problems (difficulty 1-2) before advancing to harder ones (difficulty 4-5), reducing computational waste on problems beyond a model's current capability.

Organizes 12,500 problems into 7 distinct mathematical subject categories (Prealgebra, Algebra, Number Theory, Counting and Probability, Geometry, Intermediate Algebra, Precalculus), enabling domain-specific evaluation and analysis. Each problem is tagged with its primary subject during curation, allowing researchers to isolate performance on specific mathematical domains and identify capability gaps (e.g., a model may excel at algebra but struggle with geometry). Supports both filtering and aggregation queries across subject boundaries.

Each of the 12,500 problems includes detailed step-by-step solutions that decompose the problem-solving process into intermediate reasoning steps. Solutions are provided in natural language format with mathematical notation, enabling evaluation of not just final answers but also intermediate reasoning quality. This supports training and evaluation of chain-of-thought reasoning models, where the ability to generate correct intermediate steps is as important as reaching the correct final answer. Solutions are verified by domain experts during curation, ensuring correctness.

Provides a stable, unchanging evaluation set that enables longitudinal tracking of model performance improvements over time. The dataset's fixed composition (12,500 problems) and expert-curated solutions allow researchers to compare results across different model versions, architectures, and training approaches using identical evaluation conditions. Historical performance data (e.g., GPT-3 at 6.9%, o3 and DeepSeek R1 at 90%+) is tracked and published, enabling researchers to contextualize new model performance against established baselines.

Enables construction of evaluation sets with balanced representation across the 7 mathematical subjects, ensuring that benchmark results are not skewed by subject-specific performance variations. Researchers can programmatically sample equal numbers of problems from each subject (e.g., 100 problems per subject for a 700-problem evaluation set) or weight sampling by subject difficulty distribution. This supports fair, representative evaluation that reflects overall mathematical reasoning capability rather than performance on a single domain.