构建280万条跨领域难题，提升大模型真实世界推理能力
NaturalReasoning: Reasoning in the Wild with 2.8M Challenging Questions
- 自动化生成跨学科挑战性问题与答案
- 280万题可有效迁移强模型的推理能力
- 适合训练通用推理模型的研究者使用
将推理能力从数学、编程等传统领域扩展到更广泛场景，受限于缺乏多样且高质量的问题。为此，我们提出一种可扩展的方法，用于生成多样化且具有挑战性的推理问题及参考答案。我们构建了NaturalReasoning数据集，包含280万条覆盖多个领域的题目，如物理、计算机科学、经济学、社会科学等。通过知识蒸馏实验表明，NaturalReasoning能有效激发并传递强教师模型的推理能力；此外，我们还验证其在无监督自训练中结合外部奖励模型或自奖励机制的有效性。为促进后续研究，我们已将NaturalReasoning公开发布于https://huggingface.co/datasets/facebook/natural_reasoning。
原文摘要 · Abstract (English)
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. To foster future work, we publicly release NaturalReasoning at https://huggingface.co/datasets/facebook/natural_reasoning.
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