Large language models (LLMs) have made impressive strides in various fields, but math remains a challenging hurdle. While using code to solve math problems has shown promise, the optimal way to leverage code for LLM training in mathematics is still an open question. New research explores how different coding styles in training data influence an LLM's mathematical reasoning abilities. Surprisingly, the study found that concise comments, descriptive variable names, and hardcoded solutions were most effective. While general coding knowledge was helpful, adding too much non-math-related code actually hindered performance. Similarly, supplementing code with textual explanations only benefited general-purpose LLMs, not code-specialized ones. Building on these findings, researchers developed CoinMath, a learning strategy that diversifies coding styles in training data. CoinMath significantly boosted performance on math problems compared to existing state-of-the-art models, demonstrating the potential of code-centric training for enhancing mathematical reasoning in LLMs. This breakthrough could pave the way for LLMs that truly excel in both language and logical reasoning, opening doors to applications in science, engineering, and beyond. However, challenges remain, particularly with abstract mathematical concepts, highlighting the need for further research into the interplay of code and language in AI learning.