Common-mode error in direct feedback alignment causes training stalls by saturating hidden units; this can be prevented by centering batch errors or calibrating the readout baseline, enabling faster learning without changing the core algorithm.
Direct feedback alignment trains neural networks using fixed random error projections, but gets stuck learning near a baseline predictor. The paper identifies that a shared error component across inputs drives hidden units to saturation, slowing learning. Simple fixes like centering errors or adjusting the baseline readout can prevent this collapse and speed up training.