at calculates the confidence of the generated update at each training step. The strategy calculates the residual between the exponential moving average (EMA) of the update and the current update, which represents the deviation of the approximated update. The larger the deviation, the lower the confidence in the EMA of the update.

Based on this strategy, they develop the CAME algorithm, which achieves fast convergence like traditional adaptive methods and low memory usage like memory-efficient methods. The 