Continual Learning for Generative AI: From LLMs to MLLMs and Beyond
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arXiv
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| Autores principales: | , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912551021838336 |
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| author | Guo, Haiyang Zeng, Fanhu Zhu, Fei Wang, Jiayi Wang, Xukai Zhou, Jingang Zhao, Hongbo Liu, Wenzhuo Ma, Shijie Wang, Da-Han Zhang, Xu-Yao Liu, Cheng-Lin |
| author_facet | Guo, Haiyang Zeng, Fanhu Zhu, Fei Wang, Jiayi Wang, Xukai Zhou, Jingang Zhao, Hongbo Liu, Wenzhuo Ma, Shijie Wang, Da-Han Zhang, Xu-Yao Liu, Cheng-Lin |
| contents | The rapid advancement of generative models has empowered modern AI systems to comprehend and produce highly sophisticated content, even achieving human-level performance in specific domains. However, these models are fundamentally constrained by \emph{catastrophic forgetting}, \ie~a persistent challenge where models experience performance degradation on previously learned tasks when adapting to new tasks. To address this practical limitation, numerous approaches have been proposed to enhance the adaptability and scalability of generative AI in real-world applications. In this work, we present a comprehensive survey of continual learning methods for mainstream generative AI models, encompassing large language models, multimodal large language models, vision-language-action models, and diffusion models. Drawing inspiration from the memory mechanisms of the human brain, we systematically categorize these approaches into three paradigms: architecture-based, regularization-based, and replay-based methods, while elucidating their underlying methodologies and motivations. We further analyze continual learning setups for different generative models, including training objectives, benchmarks, and core backbones, thereby providing deeper insights into the field. The project page of this paper is available at https://github.com/Ghy0501/Awesome-Continual-Learning-in-Generative-Models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_13045 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Continual Learning for Generative AI: From LLMs to MLLMs and Beyond Guo, Haiyang Zeng, Fanhu Zhu, Fei Wang, Jiayi Wang, Xukai Zhou, Jingang Zhao, Hongbo Liu, Wenzhuo Ma, Shijie Wang, Da-Han Zhang, Xu-Yao Liu, Cheng-Lin Machine Learning Computer Vision and Pattern Recognition The rapid advancement of generative models has empowered modern AI systems to comprehend and produce highly sophisticated content, even achieving human-level performance in specific domains. However, these models are fundamentally constrained by \emph{catastrophic forgetting}, \ie~a persistent challenge where models experience performance degradation on previously learned tasks when adapting to new tasks. To address this practical limitation, numerous approaches have been proposed to enhance the adaptability and scalability of generative AI in real-world applications. In this work, we present a comprehensive survey of continual learning methods for mainstream generative AI models, encompassing large language models, multimodal large language models, vision-language-action models, and diffusion models. Drawing inspiration from the memory mechanisms of the human brain, we systematically categorize these approaches into three paradigms: architecture-based, regularization-based, and replay-based methods, while elucidating their underlying methodologies and motivations. We further analyze continual learning setups for different generative models, including training objectives, benchmarks, and core backbones, thereby providing deeper insights into the field. The project page of this paper is available at https://github.com/Ghy0501/Awesome-Continual-Learning-in-Generative-Models. |
| title | Continual Learning for Generative AI: From LLMs to MLLMs and Beyond |
| topic | Machine Learning Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.13045 |