AI RESEARCH
Low-Rank Adaptation Redux for Large Models
arXiv CS.LG
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ArXi:2604.21905v1 Announce Type: new Low-rank adaptation (LoRA) has emerged as the de facto standard for parameter-efficient fine-tuning (PEFT) of foundation models, enabling the adaptation of billion-parameter networks with minimal computational and memory overhead. Despite its empirical success and rapid proliferation of variants, it remains elusive which architectural choices, optimization techniques, and deployment constraints should guide practical method selection.