Can code make AI smarter? It's a question researchers are exploring, and the results are intriguing. A new study reveals that fine-tuning large language models (LLMs) with coding data can significantly boost their reasoning abilities. Think of it like giving an AI a logic workout. Code, with its strict rules and precision, seems to sharpen an LLM's ability to solve problems, even those not directly related to programming. The research tested various LLMs, from the Llama family to Mistral and Qwen, on a range of reasoning tasks. Across the board, models trained with code performed better, showing improvements in symbolic reasoning, logical deduction, and even arithmetic. Interestingly, the type of reasoning influenced how much code helped. Symbolic tasks, like rearranging words, saw the biggest gains, while more complex logical problems benefited less. This suggests that code strengthens basic reasoning skills but might not be a magic bullet for higher-level cognition. The study also found that the optimal mix of code and regular text data varied depending on the task. Sometimes a 50/50 blend worked best, while other times a full dose of code was more effective. This highlights the need to tailor training data to the specific reasoning skills we want AI to develop. While the research focused on general reasoning, the implications are far-reaching. Imagine AI assistants that can truly understand complex instructions, or AI scientists capable of formulating hypotheses and designing experiments. Code-enhanced reasoning could unlock a new level of AI capability, paving the way for more sophisticated and helpful intelligent systems. However, challenges remain. Researchers need to explore the impact of different coding languages and the potential downsides of over-reliance on code. As we continue to push the boundaries of AI, understanding how code shapes its reasoning abilities will be crucial for building truly intelligent machines.