Nemotron-SFT-Math-v3

Dataset Description The dataset was updated on April 27th, 2026 to fix data formatting issues!** Nemotron-Math-v3 is a large-scale mathematical reasoning dataset containing model-generated reasoning trajectories produced both *with and without Python Tool-Integrated Reasoning (TIR)*. Chain-of-thought (CoT) solutions are generated using DeepSeek-V3.2-Speciale, while Python Tool-Integrated Reasoning (TIR) solutions are generated using DeepSeek-V3.2. The problems in this dataset are sourced from nvidia/Nemotron-Math-v2, which contains high-quality mathematical problems derived from the Art of Problem Solving (AoPS) community and Math StackExchange/MathOverflow forums. Each problem is solved multiple times under different reasoning regimes (including tool use vs. no tool use), and final answers are verified against the reference answers in nvidia/Nemotron-Math-v2. Only solutions whose final answers match the verified reference are included, resulting in a challenging, clean, and high-quality dataset suitable for training and evaluating mathematical reasoning systems. All components of the pipeline, including data generation, are implemented using NeMo-Skills. For detailed information, please refer to the NeMo-Skills documentation in the repository. This dataset is ready for commercial use. Dataset Owner(s): NVIDIA Corporation Dataset Creation Date: Created on: Jan 23, 2026 Last Modified on: Apr 27, 2026 License/Terms of Use: This dataset is governed by the Creative Commons Attribution 4.0 International License (CC BY 4.0) for AoPS-derived samples, while StackExchange-Math-derived samples are governed by the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0). Intended Usage This dataset is intended for: Training LLMs to perform structured mathematical reasoning Studying tool-augmented reasoning vs. pure language reasoning Building long-context or multi-trajectory reasoning systems Evaluating LLM reasoning robustness and solution diversity Re