Modality-alternating learning is a multimodal machine learning training approach where a model updates parameters for different data modalities alternately over successive training iterations rather than optimizing all modalities simultaneously. In standard joint multimodal training, dominant modalities can generate gradient signals that overshadow and suppress the optimization of weaker modalities, leading to modality competition and underutilized unimodal features. By isolating updates to one modality at a time in an alternating schedule, this paradigm ensures that representations from each distinct input type are thoroughly learned and refined, mitigating modality imbalance while still enabling effective cross-modal performance.
