Can AI truly grasp the intricacies of mathematics? While Large Language Models (LLMs) have shown promise, they often stumble when faced with complex equations or out-of-the-box problem-solving. Think of a student acing textbook examples but struggling with real-world applications. This is where ControlMath comes in, a groundbreaking approach that empowers LLMs to become true math generalists. The secret lies in generating a diverse range of math problems, going beyond simple variations on existing datasets. Imagine an AI tutor that can craft custom exercises, targeting specific areas where a student needs improvement. ControlMath works similarly by creating controllable equations and then transforming them into engaging word problems. But it doesn't stop there. ControlMath employs a clever filtering system, discarding redundant problems and keeping only those that challenge the LLM. This 'less is more' philosophy streamlines training, focusing on quality over quantity. The results? LLMs trained with ControlMath significantly outperform their peers on various math tests, showcasing a newfound ability to generalize and tackle diverse problems. This breakthrough opens doors to a future where AI can not only solve math problems but also contribute to advanced research, tutoring, and real-world problem-solving across fields like science, engineering, and finance. While challenges remain in scaling this approach and optimizing the cost of using powerful AI models for data generation, ControlMath represents a pivotal step toward unlocking AI's full mathematical potential.