Related papers: NaturalReasoning: Reasoning in the Wild with 2.8M Challenging Questions

NaturalReasoning: Reasoning in the Wild with 2.8M Challenging Questions

URL: http://arxiv.org/abs/2502.13124v1
Date: Tue, 18 Feb 2025 18:46:57 GMT
Title: NaturalReasoning: Reasoning in the Wild with 2.8M Challenging Questions
Authors: Weizhe Yuan, Jane Yu, Song Jiang, Karthik Padthe, Yang Li, Dong Wang, Ilia Kulikov, Kyunghyun Cho, Yuandong Tian, Jason E Weston, Xian Li,
Abstract summary: We present NaturalReasoning, a comprehensive dataset comprising 2.8 million questions that span multiple domains. We show that NaturalReasoning can effectively elicit and transfer reasoning capabilities from a strong teacher model. It is also effective for unsupervised self-training using external reward models or self-rewarding.
Score: 86.15997774820934
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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.

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