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Progressive Multimodal Alignment for Continual Instruction Tuning

Research Multimodal & Generative

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Merged summary

TL;DR - Progressive Multimodal Alignment (PMA) addresses projector-level forgetting during continual instruction tuning of multimodal LLMs. It selectively adds projector experts while retaining the pretrained projector as a stable alignment anchor.

  • Detects multimodal distribution shifts using a lightweight representation descriptor.
  • Expands projector experts only when needed, enabling sub-linear parameter growth.
  • Routes multimodal features across experts to balance adaptation with retention.
  • Improves prior methods across two MCIT benchmarks and multiple MLLM backbones.

Sources (1)

Progressive Multimodal Alignment for Continual Instruction Tuning

arXiv cs.CV Duzhen Zhang, Yahan Yu, Qiaoyi Su, Jiahua Dong, Tielin Zhang 2026-07-29 arXiv:2607.26947
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-08-26 14:42:33.936968 UTC

TL;DR - Progressive Multimodal Alignment (PMA) addresses projector-level forgetting during continual instruction tuning of multimodal LLMs. It selectively adds projector experts while retaining the pretrained projector as a stable alignment anchor.

  • Detects multimodal distribution shifts using a lightweight representation descriptor.
  • Expands projector experts only when needed, enabling sub-linear parameter growth.
  • Routes multimodal features across experts to balance adaptation with retention.
  • Improves prior methods across two MCIT benchmarks and multiple MLLM backbones.
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