OpenMathReasoning

OpenMathReasoning is a large-scale math reasoning dataset for training large language models (LLMs). This dataset contains

* 306K unique mathematical problems sourced from AoPS forums with: * 3.2M long chain-of-thought (CoT) solutions * 1.7M long tool-integrated reasoning (TIR) solutions * 566K samples that select the most promising solution out of many candidates (GenSelect) * Additional 193K problems sourced from AoPS forums (problems only, no solutions)

We used Qwen2.5-32B-Instruct to preprocess problems, and DeepSeek-R1 and QwQ-32B to generate solutions.

This dataset was a foundation of our winning submission to the AIMO-2 Kaggle competition.

See our paper to learn more details!

_NOTE:_ We initially reported 540K unique problems in our dataset, but this figure represented the question count at the pipeline's beginning. But the actual CoT and TIR solutions in the released dataset correspond to 306K problems. Since our OpenMath-Nemotron models were trained on this reduced subset, all published results remain reproducible with the current release—only our initial problem count was overstated.

Two factors explain this discrepancy in question numbers.

• Our filtering process removed many questions due to format restrictions (yes/no questions, multiple choice questions, etc.), benchmark decontamination, and the inability of existing LLMs to generate valid solutions for certain problems.
• A pipeline bug caused us to lose 137K proof-based questions. When we recovered and included this additional data in training, the SFT performance regressed. We are currently testing different approaches for incorporating these recovered questions and will release their solutions only if we identify clear performance improvements.

_NOTE:_ An early version of this data was released separately in Llama-Nemotron-Post-Training-Dataset.

Dataset fields

OpenMathReasoning dataset contains the following fields:

• problem: Problem statement extracted from AoPS forums and refined with Qwen2.5-32B-Instruct
• generated_solution: Synthetically generated solution using either DeepSeek-R1 or QwQ-32B
• generation_model: DeepSeek-R1 or QwQ-32B
• problem_type: Can be one of "has_answer_extracted", "no_answer_extracted" and "converted_proof" dependening on whether we were able to extract the answer or if this is a proof question converted to answer question.
• expected_answer: Extracted answer if "problem_type" is "has_answer_extracted". Otherwise this is the majority-voting answer across all generated solutions for this problem.
• problem_source: States the corresponding AoPS forum (e.g. "aops_c6_high_school_olympiads") or "MATH_training_set" as we also include a small set of generations from MATH.
• inference_mode: "cot", "tir" or "genselect"
• pass_rate_72b_tir: Pass rate out of 32 generations for Qwen2.5-Math-72B-Instruct run in TIR mode. This attribute is only available when "problem_type" is "has_answer_extracted" and is set to "n/a" for other cases.
• used_in_kaggle: Whether the instance was used in training the winning model for AIMO-2 Kaggle competition or not. We had used 2.2M CoT and 15K TIR solutions for training the OpenMath-Nemotron-14B-Kaggle model. Note that for training the OpenMath-Nemotron models, we used all the CoT, TIR, and GenSelect data, except for the TIR subset used in Kaggle.

OpenMath-Nemotron models

To demonstrate the quality of this dataset, we release a series of OpenMath-Nemotron models trained on this data.

* OpenMath-Nemotron-1.5B * OpenMath-Nemotron-7B * OpenMath-Nemotron-14B * OpenMath-Nemotron-14B-Kaggle (this is the model used in AIMO-2 Kaggle competition) * OpenMath-Nemotron-32B

!Evaluation Results

The models achieve state-of-the-art results on popular mathematical benchmarks. We present metrics as pass@1 (maj@64) where pass@1 is an average accuracy across 64 generations and maj@64 is the result of majority voting. Please see our paper for more details on the evaluation setup.

| Model | AIME24 | AIME25 | HMMT-24-25 | HLE-Math | |-------------------------------|-----------------|-------|-------|-------------| | DeepSeek-R1-Distill-Qwen-1.5B | 26.8 (60.0) | 21.4 (36.7) | 14.2 (26.5) | 2.9 (5.0) | | OpenMath-Nemotron-1.5B CoT | 61.6 (80.0) | 49.5 (66.7) | 39.9 (53.6) | 5.4 (5.4) | | OpenMath-Nemotron-1.5B TIR | 52.0 (83.3) | 39.7 (70.0) | 37.2 (60.7) | 2.5 (6.2) | | + Self GenSelect | 83.3 | 70.0 | 62.2 | 7.9 | | + 32B GenSelect | 83.3 | 70.0 | 62.8 | 8.3 | | DeepSeek-R1-Distill-Qwen-7B | 54.4 (80.0) | 38.6 (53.3) | 30.6 (42.9) | 3.3 (5.2) | | OpenMath-Nemotron-7B CoT | 74.8 (80.0) | 61.2 (76.7) | 49.7 (57.7) | 6.6 (6.6) | | OpenMath-Nemotron-7B TIR | 72.9 (83.3) | 57.5 (76.7) | 54.6 (66.3) | 7.8 (10.8) | | + Self GenSelect | 86.7 | 76.7 | 68.4 | 11.5 | | + 32B GenSelect | 86.7 | 76.7 | 69.9 | 11.9 | | DeepSeek-R1-Distill-Qwen-14B | 65.8 (80.0) | 48.4 (60.0) | 40.1 (52.0) | 4.2 (4.8) | | OpenMath-Nemotron-14B-MIX (kaggle) | 73.7 (86.7) | 57.9 (73.3) | 50.5 (64.8) | 5.7 (6.5) | | OpenMath-Nemotron-14B CoT | 76.3 (83.3) | 63.0 (76.7) | 52.1 (60.7) | 7.5 (7.6) | | OpenMath-Nemotron-14B TIR | 76.3 (86.7) | 61.3 (76.7) | 58.6 (70.9) | 9.5 (11.5) | | + Self GenSelect | 86.7 | 76.7 | 72.4 | 14.1 | | + 32B GenSelect | 90.0 | 76.7 | 71.9 | 13.7 | | QwQ-32B | 78.1 (86.7) | 66.5 (76.7) | 55.9 (63.3) | 9.0 (9.5) | | DeepSeek-R1-Distill-Qwen-32B | 66.9 (83.3) | 51.8 (73.3) | 39.9 (51.0) | 4.8 (6.0) | | OpenMath-Nemotron-32B CoT | 76.5 (86.7) | 62.5 (73.3) | 53.0 (59.2) | 8.3 (8.3) | | OpenMath-Nemotron-32B TIR | 78.4 (93.3) | 64.2 (76.7) | 59.7 (70.9) | 9.2 (12.5) | | + Self GenSelect | 93.3 | 80.0 | 73.5 | 15.7 | | DeepSeek-R1 | 79.1 (86.7) | 64.3 (73.3) | 53.0 (59.2) | 10.5 (11.4) |

Reproducing our results

The pipeline we used to produce the data and models is fully open-sourced!

• Code
• Models
• Dataset
• Paper

Citation

If you find our work useful, please consider citing us!

@article{moshkov2025aimo2,
  title   = {AIMO-2 Winning Solution: Building State-of-the-Art Mathematical Reasoning Models with OpenMathReasoning dataset},
  author  = {Ivan Moshkov and Darragh Hanley and Ivan Sorokin and Shubham Toshniwal and Christof Henkel and Benedikt Schifferer and Wei Du and Igor Gitman},
  year    = {2025},
  journal = {arXiv preprint arXiv:2504.16891}
}

Dataset Owner(s):

Release Date:

Data Version

License/Terms of Use:

Intended Usage:

Ethical Considerations:

Please report security vulnerabilities or NVIDIA AI Concerns here.