AgenticMath: 通过基于代理的数学数据生成增强LLM推理
AgenticMath: Enhancing LLM Reasoning via Agentic-based Math Data Generation
AI总结：
AgenticMath通过基于代理的数学数据生成方法，提升LLM在数学推理任务上的性能。
摘要：
高质量数据集的创建仍是一个重大挑战，因为当前方法常导致生成低质量或错误答案以及数据源信息丰富性有限。为此，我们提出AgenticMath，一种新的基于代理的方法，用于生成高质量的数学问题-答案对，以增强LLM的监督微调。我们的方法分为四个阶段：（1）种子问题过滤器选择信息丰富、复杂且清晰的问题；（2）代理问题重述步骤利用多代理系统生成多样化且逻辑一致的改写；（3）答案增强步骤通过推理链进行改写，以提高数值和逻辑正确性，不依赖于人工提供的标签；（4）最终的问题和答案评估只保留最优秀的配对。广泛的实验表明，对3B-8B参数LLM进行AgenticMath生成的数据集（仅包含30-60万数学样本）的微调，在多样化的领域内和领域外数学推理基准上，与基线模型在大量数据（如40万或230万样本）上训练的模型相比，表现相当或更优。我们的工作表明，有针对性的高质量数据生成比大规模低质量数据更有效，可以提高LLM的数学推理能力。
英文摘要：
The creation of high-quality datasets to improve Large Language Model (LLM) reasoning remains a significant challenge, as current methods often suffer from generating low-quality/incorrect answers and limited information richness from available data sources. To address this, we propose AgenticMath, a novel agentic method for generating high-quality mathematical question-answer pairs to enhance the supervised fine-tuning of LLMs. Our method operates through four stages: (1) Seed Question Filter that selects questions with high information richness, complexity, and clarity; (2) an Agentic Question Rephrase step that employs a multi-agent system to generate diverse, logically consistent paraphrases; (3) an Answer Augment step where rewrite answers using chain-of-thought reasoning to enhance numerical and logical correctness, without reliance on human-provided labels; and (4) a final Question and Answer Evaluation that retains only the most superior pairs. Extensive experiments demonstrate that, fine-tuning 3B-8B parameter LLMs on AgenticMath generated datasets (comprising only 30-60K math samples) achieves competitive or superior performance on diverse in domain and out-of-domain mathematical reasoning benchmarks compared to baselines trained on much more data (e.g., 400K or 2.3M samples). Our work demonstrates that targeted, high-quality data generation is a more efficient path to improving mathematical reasoning in LLMs than large-scale, low-quality alternatives.
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。
