Arrows of Math Reasoning Data Synthesis for Large Language Models: Diversity, Complexity and Correctness
This addresses the problem of scalable and reliable data synthesis for improving mathematical reasoning in LLMs, representing a strong domain-specific advancement.
The paper tackled the challenge of generating high-quality training data for enhancing mathematical reasoning in large language models by proposing a program-assisted synthesis framework that produced 12.3 million problem-solving triples, resulting in models achieving state-of-the-art performance on benchmark datasets.
Enhancing the mathematical reasoning of large language models (LLMs) demands high-quality training data, yet conventional methods face critical challenges in scalability, cost, and data reliability. To address these limitations, we propose a novel program-assisted synthesis framework that systematically generates a high-quality mathematical corpus with guaranteed diversity, complexity, and correctness. This framework integrates mathematical knowledge systems and domain-specific tools to create executable programs. These programs are then translated into natural language problem-solution pairs and vetted by a bilateral validation mechanism that verifies solution correctness against program outputs and ensures program-problem consistency. We have generated 12.3 million such problem-solving triples. Experiments demonstrate that models fine-tuned on our data significantly improve their inference capabilities, achieving state-of-the-art performance on several benchmark datasets and showcasing the effectiveness of our synthesis approach.