In dynamic temperature regulation, there is a trade-off between addressing the capability gap between the teacher and student models and managing the task difficulty level during the training process. When adjusting temperatures to bridge the capability gap, there is a risk of making the task too difficult for the student, leading to slower convergence and potential performance degradation. On the other hand, focusing solely on task difficulty may not effectively address the capability gap, resulting in suboptimal knowledge transfer.
To optimize this trade-off, a balanced approach is needed. One way to achieve this is by incorporating a multi-objective optimization framework that considers both the capability gap and task difficulty as simultaneous objectives. By formulating the temperature adjustment as a multi-objective optimization problem, the algorithm can dynamically adjust temperatures to find a balance between enhancing the student's learning capacity and managing the task complexity. Additionally, techniques such as curriculum learning, where the difficulty of the training samples is gradually increased, can be integrated to ensure a smooth transition for the student model while bridging the capability gap effectively.