Adaptive Curriculum Learning for Multimodal Diffusion Language Models in Biology

Biological modeling is inherently multimodal, spanning perturbations, cellular phenotypes, biomedical images, spatial structures, and textual descriptions. Multimodal diffusion language models provide a flexible, noncausal framework in these settings. However, multimodal diffusion introduces a training challenge that is largely absent from the usual unimodal view of diffusion: different prediction directions and modality-specific corruption states define distinct training problems. We introduce MaDAC, an adaptive curriculum learning method that allocates training across these multimodal prediction problems. MaDAC uses an outer curriculum to adapt prediction routes and modality-specific corruption states to the learner state, and an inner curriculum to reweight targets, while a reference distribution preserves coverage. Our analysis shows that standard corruption schedules can severely under-sample training states with asymmetric corruption across modalities, and that adaptive allocation can help when their relative utility changes during learning. We evaluate MaDAC on multimodal biological tasks spanning perturbation-phenotype modeling, biomedical imaging, and spatial segmentation. Across settings, MaDAC significantly improves over standard corruption and fixed-allocation curricula, demonstrating the value of adaptive curriculum allocation for multimodal diffusion language models for biological tasks.