Localized LoRA-MoE: Block-wise Low-Rank Experts With Adaptive Routing
Large Language Models (LLMs) and high-dimensional perception networks increasingly rely on parameter-efficient fine-tuning (PEFT) to adapt to diverse operational contexts. However, standard methods like LoRA are structurally limited by a monolithic bottleneck, making them highly susceptible to gradient warfare. Interleaved multi-task streams may trigger destructive optimization feedback, collapsing adapter weights into unspecialized averages. While recent spatial partitioning methods have introduced block-wise isolation, they remain trapped in static topologies, unable to adapt to dynamic task
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- PossiblePossibly related (embedding) · 49%baidu-baige/LoongForge →
- LinkedLinked via arxiv author · 85%Babak Barazandeh →
“Localized LoRA-MoE: Block-wise Low-Rank Experts With Adaptive Routing”
- LinkedLinked via arxiv author · 85%Subhabrata Majumdar →
“Localized LoRA-MoE: Block-wise Low-Rank Experts With Adaptive Routing”
- LinkedLinked via arxiv author · 85%Vinay Prithyani →
“Localized LoRA-MoE: Block-wise Low-Rank Experts With Adaptive Routing”
- LinkedLinked via arxiv author · 85%George Michailidis →
“Localized LoRA-MoE: Block-wise Low-Rank Experts With Adaptive Routing”