A single LoRA adapter placed at the gradient-energy-dominant shallow FFN module outperforms distributed LoRA across instruction, math, code, and conversation tasks.
Adaptive budget allocation for parameter-efficient fine-tuning
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2026 2verdicts
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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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Rethinking Adapter Placement: A Dominant Adaptation Module Perspective
A single LoRA adapter placed at the gradient-energy-dominant shallow FFN module outperforms distributed LoRA across instruction, math, code, and conversation 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%.