  Dataset distillation extracts a small set of synthetic training samples from
a large dataset with the goal of achieving competitive performance on test data
when trained on this sample. In this work, we tackle dataset distillation at
its core by treating it directly as a bilevel optimization problem.
Re-examining the foundational back-propagation through time method, we study
the pronounced variance in the gradients, computational burden, and long-term
dependencies. We introduce an improved method: Random Truncated Backpropagation
Through Time (RaT-BPTT) to address them. RaT-BPTT incorporates a truncation
coupled with a random window, effectively stabilizing the gradients and
speeding up the optimization while covering long dependencies. This allows us
to establish new state-of-the-art for a variety of standard dataset benchmarks.
A deeper dive into the nature of distilled data unveils pronounced
intercorrelation. In particular, subsets of distilled datasets tend to exhibit
much worse performance than directly distilled smaller datasets of the same
size. Leveraging RaT-BPTT, we devise a boosting mechanism that generates
distilled datasets that contain subsets with near optimal performance across
different data budgets.
