Before revision:  Assuming that we have access to the theoretically optimal model T for a task, we can directly match the task-specific model trained on S to T. S / T protocol offers a more accurate evaluation since it does not rely on possibly biased and noisy query instances. There are two challenges in putting S / T protocol into practice.
After revision:  Assuming that we have access to the theoretically optimal model T for a task, we can directly match the task-specific model trained on S to T. S / \mathcal{T / T protocol into practice.