This dataset is a mathematical problem reasoning dataset, released in 2025 by a research team from Stanford University and the University of Washington. It aims to strengthen the logical coherence of large language models (LLMs) and optimize their structured thinking through mathematical reasoning. The relevant paper results are:s1: Simple test-time scaling". This dataset contains 1,000 samples, focusing on mathematical problems and reasoning trajectories, covering multiple mathematical fields such as algebra, geometry, and probability. Each sample includes a problem description, solution steps, answer, and reasoning trajectory generated by DeepSeek r1.

Citation

@misc{muennighoff2025s1simpletesttimescaling, title={s1: Simple test-time scaling}, author={Niklas Muennighoff and Zitong Yang and Weijia Shi and Xiang Lisa Li and Li Fei-Fei and Hannaneh Hajishirzi and Luke Zettlemoyer and Percy Liang and Emmanuel Candès and Tatsunori Hashimoto}, year={2025}, eprint={2501.19393}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2501.19393}, }