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Large Language Models (LLMs) have demonstrated exceptional performance in code generation, becoming increasingly vital for software engineering and development. Recently, Chain-of-Thought (CoT) has proven effective for complex tasks by prompting LLMs to reason step-by-step and provide a final answer.However, research on *how LLMs learn to reason with CoT data for code generation* remains limited.In this work, we revisit classic CoT training, which typically learns reasoning steps before the final answer.We synthesize a dataset to separate the CoT process from code solutions and then conduct extensive experiments to study how CoT works in code generation empirically.We observe counterintuitive phenomena, suggesting that the traditional training paradigm may not yield benefits for code generation. Instead, training LLMs to generate code first and then output the CoT to explain reasoning steps for code generation is more effective.Specifically, our results indicate that a 9.86% relative performance improvement can be achieved simply by changing the order between CoT and code. Our findings provide valuable insights into leveraging CoT to enhance the reasoning capabilities of CodeLLMs and improve code generation.