LR-LoRA learns per-layer adapter ranks during training and reports outperforming fixed-rank LoRA and other PEFT baselines on language understanding and commonsense reasoning tasks.
ElaLoRA: Elastic & learnable low-rank adaptation for efficient model fine-tuning
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MoLF routes updates between full fine-tuning and LoRA at the optimizer level to match or exceed the better of the two static methods on SQL, medical QA, and counterfactual tasks while an efficient variant outperforms prior adaptive LoRA by up to 20%.
citing papers explorer
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Parameter-Efficient Fine-Tuning with Learnable Rank
LR-LoRA learns per-layer adapter ranks during training and reports outperforming fixed-rank LoRA and other PEFT baselines on language understanding and commonsense reasoning tasks.
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Beyond LoRA vs. Full Fine-Tuning: Gradient-Guided Optimizer Routing for LLM Adaptation
MoLF routes updates between full fine-tuning and LoRA at the optimizer level to match or exceed the better of the two static methods on SQL, medical QA, and counterfactual tasks while an efficient variant outperforms prior adaptive LoRA by up to 20%.