D-MoLE dynamically allocates LoRA experts to the most gradient-sensitive layers of a vision-language model for each new task, and routes inputs via per-task autoencoders, reporting near-zero forgetting on a nine-task continual learning benchmark.
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Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning
D-MoLE dynamically allocates LoRA experts to the most gradient-sensitive layers of a vision-language model for each new task, and routes inputs via per-task autoencoders, reporting near-zero forgetting on a nine-task continual learning benchmark.