TLDR: AgenticMath is a novel multi-agent pipeline designed to generate high-quality mathematical question-answer pairs. It employs a four-stage process—seed question filtering, agentic question rephrasing, answer augmentation with chain-of-thought reasoning, and rigorous evaluation—to create superior datasets. This approach enables Large Language Models to achieve competitive or better mathematical reasoning performance with significantly fewer training samples compared to methods relying on large-scale, lower-quality data, emphasizing data quality over quantity.
The advancement of Large Language Models (LLMs) has brought impressive capabilities across many fields, but they often struggle with the precise and consistent demands of mathematical reasoning. Creating high-quality datasets to train these models for math has been a significant hurdle. Current methods frequently produce low-quality or incorrect answers and don’t always capture enough rich information from available data sources.
To tackle this challenge, researchers have introduced AgenticMath, a novel system designed to generate top-tier mathematical question-answer pairs. This system aims to significantly improve how LLMs learn mathematical reasoning through a process called supervised fine-tuning. AgenticMath operates through a clever four-stage pipeline, leveraging the power of multiple AI agents to ensure quality at every step.
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The AgenticMath Pipeline: A Four-Stage Approach
The first stage is the Seed Question Filter. Here, the system carefully selects initial questions that are rich in information, complex enough to be challenging, and clearly stated. This ensures that the foundation for generating new data is strong.
Next comes the Agentic Question Rephrase step. This stage employs a multi-agent system where different agents work together to create diverse and logically consistent variations of the selected seed questions. This process introduces variety and ensures that the rephrased questions maintain their mathematical integrity.
The third stage is Answer Augment. In this phase, the system rewrites answers using a technique called chain-of-thought reasoning. This is crucial for enhancing the numerical and logical correctness of the solutions, all without needing human-provided labels. It means the system can generate detailed, step-by-step solutions autonomously.
Finally, there’s the Question and Answer Evaluation stage. This critical step ensures that only the most superior question-answer pairs are retained for training. The system assesses the clarity of the problem, the correctness of the solution, and the completeness of the reasoning, prioritizing quality and diversity.
The core idea behind AgenticMath is a “Less Is More” principle. Instead of relying on vast quantities of potentially low-quality data, this approach focuses on generating a smaller, highly curated dataset. Extensive experiments have shown that fine-tuning LLMs (specifically 3B-8B parameter models) on datasets generated by AgenticMath (comprising only 30-60K math samples) achieves performance that is competitive with or even superior to baselines trained on much larger datasets (e.g., 400K or 2.3M samples). This demonstrates that focusing on targeted, high-quality data generation is a more efficient way to improve mathematical reasoning in LLMs than simply scaling up low-quality alternatives.
The researchers have also released AgenticMathQA, a curated dataset available in 30K, 60K, and 90K versions, which emphasizes clarity, correctness, and diversity. This work highlights that data quality, supported by a rigorous multi-agent design, plays a more decisive role than dataset size in advancing mathematical reasoning in large language models. You can find the full research paper here: AgenticMath Research Paper.
