OpenCodeReasoning is a large-scale synthetic programming reasoning dataset released by NVIDIA in 2025. It aims to provide high-quality programming reasoning training data for large language models (LLMs) and promote the improvement of code generation and logical reasoning capabilities. The relevant paper results are:OpenCodeReasoning: Advancing Data Distillation for Competitive Coding". The dataset contains 735,255 samples, covering 28,319 unique programming questions, and is one of the largest reasoning programming datasets currently available.

Data source:

• It integrates questions from 11 mainstream programming platforms, including CodeForces, CodeChef, LeetCode, and public data sets such as TACO, APPS, and CodeContests.
• The code response is generated by NVIDIA's self-developed model R1 to ensure data consistency and standardization of reasoning logic.

Citation

@article{ahmad2025opencodereasoning,
title={OpenCodeReasoning: Advancing Data Distillation for Competitive Coding},
author={Wasi Uddin Ahmad, Sean Narenthiran, Somshubra Majumdar, Aleksander Ficek, Siddhartha Jain, Jocelyn Huang, Vahid Noroozi, Boris Ginsburg},
year={2025},
eprint={2504.01943},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2504.01943},
}