arXiv:2602.01990v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) achieve strong performance through instruction tuning, but real-world deployment requires them to continually expand their capabilities, making Multimodal Continual Instruction Tuning (MCIT) essential. Recent methods leverage sparse expert routing to promote task specialization, but we find that the expert routing process suffers from drift as the data distribution evolves. For example, a grounding query that previously activated localization experts may instead be routed to irrelevant experts after le
The increasing complexity and continuous deployment demands of Multimodal Large Language Models (MLLMs) necessitate robust continual learning mechanisms, which this research addresses by identifying and mitigating expert routing drift.
Improving the stability and adaptability of MLLMs in real-world, dynamic environments is crucial for their broader adoption and sustained performance, impacting various AI-driven applications.
This research provides a method to stabilize expert routing in continually updated MLLMs, enabling more reliable and efficient model evolution without performance degradation over time.
- · AI developers
- · Cloud AI platforms
- · Any industry deploying MLLMs
- · AI model infrastructure providers
- · AI models without continual learning capabilities
- · Companies relying on static AI models
More resilient and continuously improving multimodal AI systems become feasible for various applications.
The cost and effort associated with maintaining and updating complex AI models in production environments may decrease.
Accelerated development of highly adaptive, agentic AI systems that can learn and specialize on the fly across diverse data types.
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Read at arXiv cs.LG