GRIP：一种基于图的推理指令生成器
GRIP: A Graph-Based Reasoning Instruction Producer
AI总结：
本文提出基于图的推理指令生成器GRIP，利用知识图谱中的显式与隐式关系从种子数据大规模合成多样且高质量的推理指令，并构建含210万问答对的GRIP-MATH数据集，显著提升数学推理性能并降低成本。
摘要：
大规模、高质量的数据对于提升大语言模型（LLMs）的推理能力至关重要。随着公开可用的互联网数据日益稀缺，合成数据已成为一个关键的研究方向。然而，现有的数据合成方法往往存在可扩展性有限、样本多样性不足以及容易过拟合种子数据等问题，这限制了它们的实际应用价值。在本文中，我们提出了GRIP，一个基于图的推理指令生成器（Graph-based Reasoning Instruction Producer），它能够高效地合成高质量且多样化的推理指令。GRIP通过从种子数据中提取高层概念来构建知识图谱，并独特地利用图谱中的显式关系和隐式关系来驱动大规模、多样化的指令数据合成，同时采用开源多模型监督来确保数据质量。我们将GRIP应用于数学推理这一关键且具有挑战性的领域。从一个包含7.5K条数学推理样本的种子集出发，我们构建了GRIP-MATH数据集，该数据集包含210万个合成问答对。与类似的合成数据方法相比，GRIP实现了更高的可扩展性和多样性，同时显著降低了成本。在数学推理基准上，使用GRIP-MATH训练的模型相比其基础模型表现出显著提升，并大幅优于以往的数据合成方法。
英文摘要：
Large-scale, high-quality data is essential for advancing the reasoning capabilities of large language models (LLMs). As publicly available Internet data becomes increasingly scarce, synthetic data has emerged as a crucial research direction. However, existing data synthesis methods often suffer from limited scalability, insufficient sample diversity, and a tendency to overfit to seed data, which constrains their practical utility. In this paper, we present \textit{\textbf{GRIP}}, a \textbf{G}raph-based \textbf{R}easoning \textbf{I}nstruction \textbf{P}roducer that efficiently synthesizes high-quality and diverse reasoning instructions. \textit{GRIP} constructs a knowledge graph by extracting high-level concepts from seed data, and uniquely leverages both explicit and implicit relationships within the graph to drive large-scale and diverse instruction data synthesis, while employing open-source multi-model supervision to ensure data quality. We apply \textit{GRIP} to the critical and challenging domain of mathematical reasoning. Starting from a seed set of 7.5K math reasoning samples, we construct \textbf{GRIP-MATH}, a dataset containing 2.1 million synthesized question-answer pairs. Compared to similar synthetic data methods, \textit{GRIP} achieves greater scalability and diversity while also significantly reducing costs. On mathematical reasoning benchmarks, models trained with GRIP-MATH demonstrate substantial improvements over their base models and significantly outperform previous data synthesis methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
