GIST: Generator-state-Informed Supervision for Tabular Foundation Models
This research addresses sampling variability in tabular foundation models by using intermediate generator states to inform supervision. GIST jointly designs task generation and query supervision, replacing sampled hard-label losses with conditional expectations derived from these states. This method preserves the population objective and significantly improves performance across three architectures on TabArena and TALENT.