Nemotron-PrismMath Synthetic Math Reasoning
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
- cc-by-4.0
- Source / creator
- nvidia/Nemotron-PrismMath
- Collection method
- Problems and solutions were synthetically generated using Prismatic Synthesis, NVIDIA's novel pipeline for producing diverse math reasoning data. The method aims to maximize problem diversity beyond what is achievable from scraping or simple template-based generation. See the linked paper for full details on the generation process, quality filtering, and validation.
- Coverage start
- Not documented
- Coverage end
- Not documented
- Data last updated
- Not documented
- Update schedule
- Not documented
As a synthetically generated dataset, problems may contain errors or hallucinated reasoning steps not present in human-authored math data. The dataset is English-only. Coverage across math sub-domains (algebra, geometry, number theory, etc.) and difficulty levels is determined by the generation process and may be uneven — buyers should validate empirically against their target distribution. Potential overlap with common math benchmarks (GSM8K, MATH, etc.) is not explicitly documented by the source. The supplier describes synthetic or modeled records. These should not be treated as verified real-world observations.
Sample structure score: 100 / 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 | 30 of 30 top-level cells contain a value. Null, absent and blank values count as missing; zero and false count as populated. |
| Consistent value types | 30 / 30 | 30 of 30 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 |
|---|---|---|---|
| id | 0 / 10 | string | 0 / 10 |
| problem | 0 / 10 | string | 0 / 10 |
| solution | 0 / 10 | string | 0 / 10 |
About this data
Diverse math problem-solution pairs generated via Prismatic Synthesis for LLM fine-tuning and evaluation. Approximately 1M records created by NVIDIA.
Retrieve with your agent or Python
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Download the Python examplepython3 retrieve-dataset.py 3cd2488f-020b-4a84-9429-3c6bdfbc9bba --output dataset.bin
Full supplier documentation
Schema
| Name | Type | Description |
|---|---|---|
| id | VARCHAR | Unique identifier for the problem-solution pair, formatted as a 32-character hexadecimal string |
| problem | VARCHAR | Mathematical problem statement in English, may include LaTeX formatting for equations and symbols |
| solution | VARCHAR | Step-by-step worked solution with reasoning, may include LaTeX formatting and thinking process |
Sample Data
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For AI Agents
# 1. Add to your agent's MCP config (claude_desktop_config.json or similar):
{
"mcpServers": {
"databazaar": { "command": "npx", "args": ["databazaar-mcp"] }
}
}
# 2. Your agent can then call:
search_datasets({ query: "Nemotron-PrismMath Synthetic M" })
// Found: 3cd2488f-020b-4a84-9429-3c6bdfbc9bba
get_download_url({ dataset_id: "3cd2488f-020b-4a84-9429-3c6bdfbc9bba" }) // free — sign in with MCP OAuth first# Free dataset — sign in or use your account API key: curl https://api.databazaar.io/datasets/3cd2488f-020b-4a84-9429-3c6bdfbc9bba/download-url -H "Authorization: Bearer $DATABAZAAR_API_KEY"