OpenThoughts-114k is an open source reasoning dataset that focuses on areas such as mathematics, code, science, and puzzles, and contains 114,000 high-quality samples. The dataset was released by Open Thoughts in 2025 and aims to train small reasoning models to outperform existing large models (such as DeepSeek-R1-Distill-Qwen-32B and DeepSeek-R1-Distill-Qwen-7B) on mathematical and code reasoning tasks.

Citation

@misc{guha2025openthoughtsdatarecipesreasoning, title={OpenThoughts: Data Recipes for Reasoning Models}, author={Etash Guha and Ryan Marten and Sedrick Keh and Negin Raoof and Georgios Smyrnis and Hritik Bansal and Marianna Nezhurina and Jean Mercat and Trung Vu and Zayne Sprague and Ashima Suvarna and Benjamin Feuer and Liangyu Chen and Zaid Khan and Eric Frankel and Sachin Grover and Caroline Choi and Niklas Muennighoff and Shiye Su and Wanjia Zhao and John Yang and Shreyas Pimpalgaonkar and Kartik Sharma and Charlie Cheng-Jie Ji and Yichuan Deng and Sarah Pratt and Vivek Ramanujan and Jon Saad-Falcon and Jeffrey Li and Achal Dave and Alon Albalak and Kushal Arora and Blake Wulfe and Chinmay Hegde and Greg Durrett and Sewoong Oh and Mohit Bansal and Saadia Gabriel and Aditya Grover and Kai-Wei Chang and Vaishaal Shankar and Aaron Gokaslan and Mike A. Merrill and Tatsunori Hashimoto and Yejin Choi and Jenia Jitsev and Reinhard Heckel and Maheswaran Sathiamoorthy and Alexandros G. Dimakis and Ludwig Schmidt}, year={2025}, eprint={2506.04178}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2506.04178}, }