The field of multimodal learning is moving towards addressing the challenges of modality imbalance and noise interference. Researchers are exploring new learning paradigms, such as negative learning, to preserve modality-specific information and improve the robustness of multimodal models. Additionally, there is a growing interest in optimizing multimodal learning using techniques like variance reduction and spectral descent. Noteworthy papers in this area include Multimodal Negative Learning, which introduces a dynamic guidance mechanism for negative learning, and MARS-M, which integrates variance reduction with matrix-based preconditioned optimizers. Other notable works, such as Modality-Aware SAM and Contribution-Guided Asymmetric Learning, propose innovative approaches to modality-aware optimization and robust multimodal fusion. These advancements have the potential to significantly improve the performance and generalization ability of multimodal models in various applications.