The extensive reproduction research on DeepSeek-R1 has revealed that combining supervised fine-tuning (SFT) with reinforcement learning (RLVR) can significantly bolster the reasoning capabilities of language models. This research comprehensively details data preparation methodologies, training techniques, and the design of reward mechanisms. Furthermore, it anticipates that these reasoning-enhanced language models will exhibit vast potential in safety, multimodal, and multilingual applications, thereby laying a solid foundation and charting a clear direction for future advancements in this field.