  Machine Reading Comprehension (MRC) reveals the ability to understand a given
text passage and answer questions based on it. Existing research works in MRC
rely heavily on large-size models and corpus to improve the performance
evaluated by metrics such as Exact Match ($EM$) and $F_1$. However, such a
paradigm lacks sufficient interpretation to model capability and can not
efficiently train a model with a large corpus. In this paper, we argue that a
deep understanding of model capabilities and data properties can help us feed a
model with appropriate training data based on its learning status.
Specifically, we design an MRC capability assessment framework that assesses
model capabilities in an explainable and multi-dimensional manner. Based on it,
we further uncover and disentangle the connections between various data
properties and model performance. Finally, to verify the effectiveness of the
proposed MRC capability assessment framework, we incorporate it into a
curriculum learning pipeline and devise a Capability Boundary Breakthrough
Curriculum (CBBC) strategy, which performs a model capability-based training to
maximize the data value and improve training efficiency. Extensive experiments
demonstrate that our approach significantly improves performance, achieving up
to an 11.22% / 8.71% improvement of $EM$ / $F_1$ on MRC tasks.
