Architecture-Agnostic Curriculum Learning for Document Understanding: Empirical Evidence from Text-Only and Multimodal Paradigms

This study investigates whether the efficiency gains afforded by curriculum learning transfer across architecturally distinct document understanding models. Through a series of 24 controlled experiments comparing BERT (text-only) and LayoutLMv3 (multimodal) on the FUNSD and CORD benchmarks, we demonstrate that progressive data scheduling following a 33%→67%→100% trajectory yields consistent training speedups of 34.9% for BERT and 29.2% for LayoutLMv3, both achieving statistical significance at p < 0.001. The modest 5.8 percentage point differential between these architectures provides compelling evidence for architectureagnostic curriculum benefits. Notably, both model families exhibit approximately twofold higher final training loss while maintaining equivalent downstream F1 performance, suggesting that the loss differential reflects optimization dynamics rather than representational degradation. Extended validation across six document domains confirms the cross-domain transferability of these findings. These results indicate that curriculum learning operates primarily at the data distribution level, enabling practitioners to deploy uniform progressive schedules across heterogeneous model portfolios without requiring architecture-specific tuning.