  Meta-learning can extract an inductive bias from previous learning experience
and assist the training of new tasks. It is often realized through optimizing a
meta-model with the evaluation loss of task-specific solvers. Most existing
algorithms sample non-overlapping $\mathit{support}$ sets and $\mathit{query}$
sets to train and evaluate the solvers respectively due to simplicity
($\mathcal{S}$/$\mathcal{Q}$ protocol). Different from
$\mathcal{S}$/$\mathcal{Q}$ protocol, we can also evaluate a task-specific
solver by comparing it to a target model $\mathcal{T}$, which is the optimal
model for this task or a model that behaves well enough on this task
($\mathcal{S}$/$\mathcal{T}$ protocol). Although being short of research,
$\mathcal{S}$/$\mathcal{T}$ protocol has unique advantages such as offering
more informative supervision, but it is computationally expensive. This paper
looks into this special evaluation method and takes a step towards putting it
into practice. We find that with a small ratio of tasks armed with target
models, classic meta-learning algorithms can be improved a lot without
consuming many resources. We empirically verify the effectiveness of
$\mathcal{S}$/$\mathcal{T}$ protocol in a typical application of meta-learning,
$\mathit{i.e.}$, few-shot learning. In detail, after constructing target models
by fine-tuning the pre-trained network on those hard tasks, we match the
task-specific solvers and target models via knowledge distillation.
