Objective <p>This study endeavors to develop an intelligent diagnosis and treatment model for Traditional Chinese Medicine (TCM) syndrome differentiation and treatment, characterized by robust reasoning capabilities and exceptional reliability. This is achieved by harnessing clinical case data, employing knowledge distillation methodologies, and integrating Direct Policy Optimization (DPO) reinforcement learning techniques.</p> Methods <p>GPT-4o was employed as the teacher model to perform knowledge distillation on TCM clinical case data, thereby generating a high-quality instruction dataset for syndrome differentiation and treatment. The distilled data was subsequently fine-tuned by Low-Rank Adaptation (LoRA) method grounded in the Qwen2.5-7B model to improve its abilities of diagnosis and treatment, together with competencies of personalized syndrome differentiation and treatment. Furthermore, an additional dataset of clinical cases was leveraged to emulate the diagnosis and treatment preferences of TCM experts, with the DPO reinforcement learning technique being applied for ongoing refinement and enhancement.</p> Results <p>The recommended knowledge distillation approach maintained prescription recommendation performance comparable to direct training upon the reinforcement of the model’s interpretability and generalization on external data. Aside from that, the DPO method ameliorated the stability of prediction results on the external dataset.</p> Conclusion <p>The TCM Large Language Model constructed with knowledge distillation and reinforcement learning strategies effectively enhances diagnosis and treatment reasoning and personalized syndrome differentiation and treatment competencies. This approach provides new research directions and technical support for intelligent TCM clinical decision-making.</p>