| # Full-dataset and commercial access |
|
|
| AIME++ is built for model teams that need large volumes of difficult mathematical reasoning problems with a transparent, deterministic success condition. Each problem resolves to an integer from `0` through `999`, enabling exact-match evaluation and RLVR rewards without a learned judge. |
|
|
| This repository is a 157-problem inspection and integration sample released under the MIT License. Teams may use it to validate their data pipeline, run initial model experiments, test exact-match rewards, and inspect the progression from competition-style to research-level mathematics. |
|
|
| ## Available scale |
|
|
| Ulam AI's broader database contains more than 100,000 problems. Available AIME++ collections include: |
|
|
| | Collection | Available volume | Description | |
| |---|---:|---| |
| | AIME family | 24,700+ problems | Standard AIME and AIME Hard problems; graduate and research-level problems are excluded from this count | |
| | AIME-Graduate | 1,000+ problems | Graduate-level problems with the same `0–999` answer contract | |
| | AIME-Researcher | 100+ problems | Research-level problems with the same `0–999` answer contract | |
|
|
| The public sample represents approximately 132 AIME-family examples, 20 graduate examples, and 5 researcher examples. It is large enough for technical diligence while preserving the overwhelming majority of the catalog for licensed delivery. |
|
|
| ## Why teams buy the larger collection |
|
|
| - **Production scale:** tens of thousands of consistent prompt-and-answer pairs rather than a small benchmark. |
| - **Deterministic rewards:** every item supports inexpensive, reproducible exact-match scoring. |
| - **Difficulty coverage:** the same output contract spans AIME, AIME Hard, AIME-Graduate, and AIME-Researcher tasks. |
| - **Low integration cost:** JSONL, stable IDs, explicit tiers, a JSON Schema, validation tooling, and a reference scorer are already defined in the sample. |
| - **Internal authorship:** the problems were created internally by Ulam AI, which holds the dataset rights. |
| - **Flexible deployment:** collections can support supervised fine-tuning, RLVR, capability evaluation, regression suites, and custom tier mixes under the applicable commercial terms. |
|
|
| The dataset is intentionally answer-only. Each supplied final answer is the authoritative golden solution for its problem. Worked derivations and chain-of-thought traces are not part of the product format. |
|
|
| ## Commercial delivery can be scoped by |
|
|
| - collection and number of problems; |
| - AIME versus AIME Hard composition within the AIME family; |
| - AIME-Graduate and AIME-Researcher volume; |
| - training, fine-tuning, distillation, RLVR, or internal-evaluation use; |
| - public-answer versus separated or sealed answer-key delivery; |
| - custom topic or difficulty targeting; |
| - JSONL, Parquet, or buyer-specific export format; |
| - versioning, validation reports, updates, and support; and |
| - private evaluation execution and scorecards. |
|
|
| ## Information to include in an inquiry |
|
|
| To make scoping fast, prospective partners should provide: |
|
|
| - intended use; |
| - desired problem count and tier mix; |
| - whether the answer key should be delivered, separated, or retained by Ulam; |
| - model and tool-access constraints; |
| - required delivery format and security controls; |
| - desired evaluation or update cadence; and |
| - target date and license term. |
|
|
| Visit [ulam.ai](https://ulam.ai/) to contact Ulam AI. Final inventory, deliverables, usage rights, pricing, representations, and support are defined in the applicable written agreement. |
|
|
