  Dataset Distillation is used to create a concise, yet informative, synthetic
dataset that can replace the original dataset for training purposes. Some
leading methods in this domain prioritize long-range matching, involving the
unrolling of training trajectories with a fixed number of steps (NS) on the
synthetic dataset to align with various expert training trajectories. However,
traditional long-range matching methods possess an overfitting-like problem,
the fixed step size NS forces synthetic dataset to distortedly conform seen
expert training trajectories, resulting in a loss of generality-especially to
those from unencountered architecture. We refer to this as the Accumulated
Mismatching Problem (AMP), and propose a new approach, Automatic Training
Trajectories (ATT), which dynamically and adaptively adjusts trajectory length
NS to address the AMP. Our method outperforms existing methods particularly in
tests involving cross-architectures. Moreover, owing to its adaptive nature, it
exhibits enhanced stability in the face of parameter variations.
