  We investigate the impact of dataset-specific hyperparameter, feature
encoding, and architecture tuning on five recent model families for tabular
data generation through an extensive benchmark on 16 datasets. This study
addresses the practical need for a unified evaluation of models that fully
considers hyperparameter optimization. Additionally, we propose a reduced
search space for each model that allows for quick optimization, achieving
nearly equivalent performance at a significantly lower cost.Our benchmark
demonstrates that, for most models, large-scale dataset-specific tuning
substantially improves performance compared to the original configurations.
Furthermore, we confirm that diffusion-based models generally outperform other
models on tabular data. However, this advantage is not significant when the
entire tuning and training process is restricted to the same GPU budget for all
models.
