Aletheia introduces gradient-guided layer selection for LoRA fine-tuning, a method to identify which neural network layers benefit most from Low-Rank Adaptation. By using gradient information rather than applying LoRA uniformly, the approach reduces computational cost while maintaining performance across different architectures.
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Aletheia: Gradient-Guided Layer Selection for Efficient LoRA Fine-Tuning Across Architectures
Gradient-guided layer selection lets LoRA concentrate fine-tuning only on high-impact layers, cutting computational costs while preserving performance across architectures.
Monday, April 20, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.LG (Machine Learning)BY sys://pipeline
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