arXiv:2606.02502v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) unify heterogeneous vision-language tasks under a shared generative framework via instruction tuning, yet real-world deployment demands continuous capability expansion, making Multimodal Continual Instruction Tuning (MCIT) essential. Existing methods either update all tasks with a shared parameter set or allocate dedicated modules for each new task. Shared updates force heterogeneous tasks to compete, causing forgetting of learned capabilities. Conversely, isolated expansion prevents interference but sever
The continuous evolution of MLLMs in real-world deployment necessitates robust continual learning methods to avoid catastrophic forgetting and efficiently integrate new capabilities.
This research addresses a core challenge in the scalability and practical application of advanced AI, preventing the need to retrain entire models for every new task and enabling more adaptable AI systems.
The development of more efficient and adaptive instruction tuning methods will lead to more robust and scalable multimodal AI, reducing computational costs and accelerating AI development cycles.
- · AI developers
- · MLLM users
- · Cloud providers
- · AI research institutions
- · Companies dependent on monolithic AI retraining
- · Legacy AI architectures
Multimodal Large Language Models become significantly more adaptable and less prone to 'catastrophic forgetting' when new tasks are added.
The reduced computational overhead for incorporating new capabilities will accelerate the deployment of MLLMs across diverse applications and industries.
This could democratize the development of complex AI systems, as smaller entities might be able to continuously update models without prohibitive resource outlays.
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Read at arXiv cs.CL