Tabular regression objectives get a fresh ranking: quantile loss is steadier on four backbones, while early stopping stalls between noise regions
A systematic re-evaluation across 41 tabular datasets under one protocol finds quantile loss gives more consistent test-RMSE gains over tuned mean squared error on MLP, TabPack, RealMLP and TabM, and reveals that regions with different noise levels prefer different stopping epochs.