In DTKD, the reference temperature plays a crucial role in determining the softness of the labels used in knowledge distillation. To adaptively select the reference temperature and further enhance the performance of DTKD, a dynamic approach can be implemented. One way to achieve this is by incorporating a feedback loop mechanism that continuously monitors the performance of the student model during training. Based on the student's learning progress and the convergence rate, the reference temperature can be adjusted dynamically to optimize the knowledge transfer process. Additionally, techniques such as reinforcement learning or meta-learning can be employed to learn the optimal reference temperature based on the specific characteristics of the dataset and the models involved. By continuously evaluating the effectiveness of different reference temperatures and adjusting them in real-time, DTKD can adapt to the changing dynamics of the training process and improve its overall performance.