Core Viewpoint - The article emphasizes the importance of continual learning in generative AI and multimodal large models, addressing the challenges posed by dynamic environments and the "catastrophic forgetting" phenomenon when learning new tasks [5][11][43]. Summary by Sections Research Motivation - The rapid development of generative AI models, particularly large models, has enabled modern intelligent systems to understand and generate complex content, achieving near-human performance in some areas. However, these models face the challenge of "catastrophic forgetting," where learning new tasks significantly degrades performance on previously learned tasks. Various methods have been proposed to enhance the adaptability and scalability of generative AI in practical applications [5][11]. Research Content - The article systematically reviews continual learning methods for generative AI, covering large language models (LLMs), multimodal large language models (MLLMs), visual-language action models (VLA), and diffusion models. The focus is on training objectives, application scenarios, and technical methods, including architecture expansion, regularization, and replay strategies to balance new task learning with the retention of old task performance. Evaluation metrics and future directions are also discussed [8][10][11]. Multimodal Large Model Continual Learning: Benchmark and Methods - The article identifies two key challenges in continual learning for multimodal large models: the overlap of existing evaluation benchmarks with pre-training data, leading to distorted results, and the difficulty in balancing new task learning with old task forgetting. A new UCIT evaluation benchmark is proposed, along with a hierarchical decoupled learning strategy to address catastrophic forgetting in continual instruction tuning [13][18]. Research Methods - The article introduces the HiDe-LLaVA model, which employs a hierarchical processing mechanism to adaptively select tasks and retain shared knowledge across tasks. Experimental results indicate that this method effectively mitigates catastrophic forgetting while balancing model performance and computational efficiency [13][14]. Future Directions - The article outlines the development of the MCITlib, an open-source multimodal continual instruction tuning library and benchmark, which integrates mainstream algorithms and high-quality benchmarks to provide a standardized evaluation platform for researchers. Future updates will expand the library to include more models, tasks, and evaluation dimensions [41][42]. Conclusion and Outlook - The ability to enable continual learning in generative AI, represented by multimodal large models, is a significant step towards achieving generalized artificial intelligence. The article aims to provide comprehensive support for researchers and developers in this field through systematic reviews, benchmarks, cutting-edge methods, and open-source tools [44].