Imagine an AI that could not only solve math problems but also double-check its work and explain its reasoning. That's the intriguing concept behind new research exploring how Large Language Models (LLMs) can be transformed into powerful mathematical verifiers. While LLMs like GPT-4 have shown impressive abilities in various domains, math remains a significant challenge. Existing approaches often rely on simple binary feedback (correct/incorrect) to train these models. This approach, however, lacks the depth needed for true understanding. The researchers propose a novel technique to boost the accuracy of mathematical verifiers: detailed natural language feedback. This feedback goes beyond simply labeling a solution as right or wrong; instead, it provides step-by-step explanations, pointing out the precise location of any errors and the underlying reasons. Think of it as having a meticulous tutor guiding the LLM's learning process. The new approach, known as MATH-Minos, uses a two-stage training process. First, it leverages this detailed feedback to refine the LLM's evaluation skills. In the second stage, it reverts to traditional binary feedback for faster processing during actual use. The results are promising. MATH-Minos significantly outperforms existing methods on benchmark math datasets, demonstrating that a richer learning process leads to a more reliable verifier. This technology has far-reaching implications. By boosting the reliability of AI in math, researchers hope to create systems that can provide not just answers, but also the ability to understand and explain their reasoning, just like a human mathematician. While further research is needed to fully realize this potential, it's an important step towards building AI systems we can trust with complex tasks requiring rigorous logical thought.