Can smaller language models truly tackle complex math problems? New research suggests they can, challenging the notion that massive models are the sole key to advanced mathematical reasoning. The Skywork-Math project introduces a novel two-stage training approach that significantly boosts the math skills of smaller Large Language Models (LLMs), achieving impressive results on challenging benchmarks. The secret? A massive, 2.5-million-instance dataset called Skywork-MathQA, created using advanced data synthesis techniques. Researchers found that carefully scaling the training data, focusing on both the diversity and difficulty of math problems, was key to unlocking the potential of these smaller LLMs. The Skywork-Math 7B models, trained on this dataset, performed remarkably well, exceeding expectations and even outperforming some larger models and an early version of GPT-4 on certain tests. This discovery has broader implications for the field of AI. It suggests that focusing on strategic data scaling and problem diversity can be just as effective as simply increasing model size. This approach not only unlocks hidden potential within smaller, more accessible LLMs, but also hints at more efficient ways to develop powerful AI systems for mathematical reasoning. The next step? Integrating code-based calculations and expanding these techniques to other complex reasoning tasks, paving the way for even more versatile and capable LLMs in the future.