textAI-MO/NuminaMath-1.5mathreasoningchain-of-thoughtpost-trainingfine-tuningolympiadcompetition-mathllm-training

NuminaMath 1.5 Competition Math Problems

Free

Open dataset

Sample structure: 99.3 / 100

2 download links issued

Seller: DataBazaar

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Source, license and coverage

Supplier documentation. These claims are separate from the automated sample score. A listing edit date is not a data freshness date.

License: apache-2.0
Source / creator: huggingface: AI-MO/NuminaMath-1.5
Collection method: auto_imported_huggingface_federated
Coverage start: Not documented
Coverage end: Not documented
Data last updated: Not documented
Update schedule: Not documented

Source documentation ↗

License terms ↗

Sample structure score: 99.3 / 100

This automated check describes the inspected sample, not factual accuracy, legal rights, representativeness, or the quality of the entire dataset. It is not a customer rating.

Assessed 10 sample records (JSON) on 2026-10-09. All records in the provided sample were checked.

Check	Points	Evidence
Populated cells	50 / 50	90 of 90 top-level cells contain a value. Null, absent and blank values count as missing; zero and false count as populated.
Consistent value types	29.3 / 30	88 of 90 populated cells match their column's most common observed type. Types are inferred, not checked against real-world truth.
Consistent record shape	20 / 20	10 of 10 records have the expected fields. CSV/TSV use the header width; JSON uses the union of observed keys.

Field-level findings and improvements

Check missing cells and mixed types below. Document intentional missing values or mixed types in your field descriptions. Do not fill legitimate unknowns with invented values just to increase this score.

Field	Missing cells	Most common type	Other populated types
problem	0 / 10	string	0 / 10
solution	0 / 10	string	0 / 10
answer	0 / 10	number	2 / 10
problem_type	0 / 10	string	0 / 10
question_type	0 / 10	string	0 / 10
problem_is_valid	0 / 10	string	0 / 10
solution_is_valid	0 / 10	string	0 / 10
source	0 / 10	string	0 / 10
synthetic	0 / 10	boolean	0 / 10

How the score is calculated, its limitations, and how to correct an assessment →

About this data

Competition-level math problems with Chain-of-Thought solutions, sourced from Chinese high school exercises through international olympiads. Suitable for math reasoning fine-tuning and retrieval-augmented generation.

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Download the Python example

python3 retrieve-dataset.py 71c82584-0983-404c-aef3-8952d62f9b42 --output dataset.bin

Full supplier documentation

Original supplier listing: NuminaMath 1.5 — 900K Competition Math Problems with Chain-of-Thought Solutions ~900K competition-level math problems with Chain-of-Thought solutions, sourced from Chinese high school exercises through international olympiads. Apache 2.0, parquet format, ideal for math reasoning fine-tuning and RAG.

Schema

Name	Type	Description
problem	VARCHAR	Natural language statement of a competition-level math problem, potentially multi-line with LaTeX notation.
solution	VARCHAR	Step-by-step Chain-of-Thought reasoning leading to the answer, formatted with LaTeX math expressions.
answer	VARCHAR	The numerical or symbolic final answer to the problem, may include LaTeX formatting.
problem_type	VARCHAR	Mathematical domain classification (e.g., Geometry, Algebra, Number Theory, Combinatorics).
question_type	VARCHAR	Format category of the problem (e.g., math-word-problem, proof, calculation).
problem_is_valid	VARCHAR	Binary validity flag for problem statement: Yes or No.
solution_is_valid	VARCHAR	Binary validity flag for solution correctness: Yes or No.
source	VARCHAR	Origin identifier for the problem (e.g., orca_math, olympiad collection, textbook name).
synthetic	BOOLEAN	Boolean indicating whether the problem was generated synthetically (true) or sourced from existing materials (false).

Sample Data

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