NaturalReasoning: Reasoning in the Wild with 2.8M Challenging Questions

Weizhe Yuan, Jane Yu, Song Jiang, Karthik Padthe, Yang Li, Dong Wang, Ilia Kulikov, Kyunghyun Cho, Yuandong Tian, Jason E Weston, Xian Li

Keywords: large language models, reasoning

TL;DR: We introduce NaturalReasoning, a 2.8M-question dataset spanning diverse domains, enabling effective knowledge distillation and unsupervised self-training to enhance LLM reasoning capabilities.

Abstract: Scaling reasoning capabilities beyond traditional domains such as math and coding is hindered by the lack of diverse and high-quality questions. To overcome this limitation, we introduce a scalable approach for generating diverse and challenging reasoning questions, accompanied by reference answers. We present NaturalReasoning, a comprehensive dataset comprising 2.8 million questions that span multiple domains, including STEM fields (e.g., Physics, Computer Science), Economics, Social Sciences, and more. We demonstrate the utility of the questions in NaturalReasoning through knowledge distillation experiments which show that NaturalReasoning can effectively elicit and transfer reasoning capabilities from a strong teacher model. Furthermore, we demonstrate that NaturalReasoning is also effective for unsupervised self-training using external reward models or self-rewarding.

Dataset URL: https://huggingface.co/datasets/facebook/natural_reasoning

Primary Area: Datasets & Benchmarks for applications in language modeling and vision language modeling

Submission Number: 2285