OpenMath – Nemotron – NVIDIA’s Open – source Mathematical Reasoning Model Series

What is OpenMath-Nemotron?

OpenMath-Nemotron is a series of open-source mathematical reasoning models released by NVIDIA, specifically designed to tackle complex mathematical problems, including those at the Olympiad level.
These models are trained on the large-scale OpenMathReasoning dataset, which contains 540,000 unique problems and 3.2 million long-form reasoning solutions.
The OpenMath-Nemotron series includes OpenMath-Nemotron-1.5B, OpenMath-Nemotron-7B, OpenMath-Nemotron-14B, and OpenMath-Nemotron-32B, as well as OpenMath-Nemotron-14B-Kaggle (the model used in the AIMO-2 competition).
Notably, the 1.5B version outperforms the 14B DeepSeek-R1 model on certain tasks.

Key Features of OpenMath-Nemotron

• Solving Complex Mathematical Problems:
  Capable of handling problems ranging from basic mathematics to Olympiad-level challenges.

• Long-Form Reasoning:
  Generates detailed step-by-step solutions through chain-of-thought processes.

• Multi-Modal Reasoning:
  Supports different types of reasoning strategies, adapting to diverse mathematical problem types.

Technical Principles Behind OpenMath-Nemotron

• Large-Scale Dataset:
  Trained on the OpenMathReasoning dataset, which includes 540,000 unique math problems and 3.2 million long-form solutions, sourced from the Art of Problem Solving (AoPS) community forums after strict filtering and processing.

• Chain-of-Thought (CoT) Reasoning:
  Models reason step-by-step by generating intermediate solution steps before arriving at the final answer, enabling deeper analytical thinking.

• Tool-Integrated Reasoning (TIR):
  Through iterative training, generation, and quality filtering, TIR integrates code execution with long-form reasoning.
  The model can generate code snippets when necessary, execute them in a secure sandbox, and produce more accurate solutions.

• Model Training and Optimization:
  Based on supervised fine-tuning (SFT) of the Qwen2.5-Base model, supporting multiple tasks including CoT solution generation, TIR solution generation, and GenSelect.
  Training incorporates the AdamW optimizer, cosine learning rate decay, sequence packing, and context parallelism techniques to accelerate long-form reasoning training.

• Inference Optimization:
  Model inference is optimized with TensorRT-LLM, supporting dynamic batching and multiple quantization techniques such as int8 and FP8, significantly improving inference speed and reducing latency.

Project Resources

• GitHub Repository: https://github.com/NVIDIA/NeMo-Skills

• Hugging Face Models Collection: https://huggingface.co/collections/nvidia/openmathreasoning

• arXiv Technical Paper: https://arxiv.org/pdf/2504.16891

Application Scenarios

• Mathematics Education:
  Assists students and teachers in solving math problems and enhancing learning outcomes.

• Competition Training:
  Helps math competition participants practice and refine their problem-solving strategies.

• Academic Research:
  Supports exploration of complex mathematical topics and aids academic investigations.

• Industrial Applications:
  Solves challenging engineering and financial mathematics problems to improve operational efficiency.

• AI Development:
  Serves as a foundational model for developing AI systems requiring advanced mathematical reasoning.