In traditional knowledge distillation, the teacher and student model architectures are almost always pre-fixed in size and structure. This fixed configuration often results in a model capacity gap, where the smaller student cannot effectively absorb the larger teacher's knowledge. Furthermore, standard distillation frameworks lack a systematic methodology to design these architectures and do not explain how the pre-fixed model setups constrain the architectural choices.