Nemotron-Math-v2 is a mathematical inference dataset released by NVIDIA Corporation in 2025. Related research papers include... Nemotron-Math: Efficient Long-Context Distillation of Mathematical Reasoning from Multi-Mode Supervision It is primarily used to train LLMs to perform structured mathematical reasoning, to study the differences between tool-enhanced reasoning and pure language reasoning, and to build long-context or multi-track reasoning systems. This dataset contains approximately 347,000 high-quality mathematical problems and 7 million model-generated inference trajectories. Each problem is solved in six configurations: high/medium/low inference depth and with or without Python TIR, and the answers are validated via a pipeline using an LLM as the arbiter. Data Fields:

• Problem: Problem statements extracted from sources such as OpenMathReasoning and MathStackExchange.
• Messages: The user's and assistant's conversation log, used for LLM training.
• expected_answer: The extracted answer or the majority vote answer generated by the model.
• metadata: Pass rate under different reasoning and tool usage scenarios
• data_source: Data source is AoPS or StackExchange-Math
• tool: The tool definition used, or empty.

Citation

@article{du2025nemotronmath, title = {Nemotron-Math: Efficient Long-Context Distillation of Mathematical Reasoning from Multi-Mode Supervision}, author = {Du, Wei and Toshniwal, Shubham and Kisacanin, Branislav and Mahdavi, Sadegh and Moshkov, Ivan and Armstrong, George and Ge, Stephen and Minasyan, Edgar and Chen, Feng and Gitman, Igor}, journal = {arXiv preprint arXiv:2512.15489}, year = {2025} }