RV-Syn: Rational and Verifiable Mathematical Reasoning Data Synthesis based on Structured Function Library

• URL: http://arxiv.org/abs/2504.20426v1
• Date: Tue, 29 Apr 2025 04:42:02 GMT
• Title: RV-Syn: Rational and Verifiable Mathematical Reasoning Data Synthesis based on Structured Function Library
• Authors: Jiapeng Wang, Jinhao Jiang, Zhiqiang Zhang, Jun Zhou, Wayne Xin Zhao,
• Abstract summary: RV-Syn is a novel mathematical Synthesis approach.<n>It generates graphs as solutions by combining Python-formatted functions from this library.<n>Based on the constructed graph, we achieve solution-guided logic-aware problem generation.
• Score: 58.404895570822184
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The advancement of reasoning capabilities in Large Language Models (LLMs) requires substantial amounts of high-quality reasoning data, particularly in mathematics. Existing data synthesis methods, such as data augmentation from annotated training sets or direct question generation based on relevant knowledge points and documents, have expanded datasets but face challenges in mastering the inner logic of the problem during generation and ensuring the verifiability of the solutions. To address these issues, we propose RV-Syn, a novel Rational and Verifiable mathematical Synthesis approach. RV-Syn constructs a structured mathematical operation function library based on initial seed problems and generates computational graphs as solutions by combining Python-formatted functions from this library. These graphs are then back-translated into complex problems. Based on the constructed computation graph, we achieve solution-guided logic-aware problem generation. Furthermore, the executability of the computational graph ensures the verifiability of the solving process. Experimental results show that RV-Syn surpasses existing synthesis methods, including those involving human-generated problems, achieving greater efficient data scaling. This approach provides a scalable framework for generating high-quality reasoning datasets.

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