  Robust loss functions are designed to combat the adverse impacts of label
noise, whose robustness is typically supported by theoretical bounds agnostic
to the training dynamics. However, these bounds may fail to characterize the
empirical performance as it remains unclear why robust loss functions can
underfit. We show that most loss functions can be rewritten into a form with
the same class-score margin and different sample-weighting functions. The
resulting curriculum view provides a straightforward analysis of the training
dynamics, which helps attribute underfitting to diminished average sample
weights and noise robustness to larger weights for clean samples. We show that
simple fixes to the curriculums can make underfitting robust loss functions
competitive with the state-of-the-art, and training schedules can substantially
affect the noise robustness even with robust loss functions. Code is available
at \url{github}.
