Aletheia: Gradient-Guided Layer Selection for Efficient LoRA Fine-Tuning Across Architectures

• URL: http://arxiv.org/abs/2604.15351v1
• Date: Sat, 04 Apr 2026 10:24:12 GMT
• Title: Aletheia: Gradient-Guided Layer Selection for Efficient LoRA Fine-Tuning Across Architectures
• Abstract summary: Low-Rank Adaptation (LoRA) has become the dominant parameter-efficient fine-tuning method for large models.<n>We introduce Aletheia, a gradient-guided layer selection method.<n>We show that Aletheia can make LoRA fine-tuning materially more efficient without introducing major downstream damage on the evaluated set.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low-Rank Adaptation (LoRA) has become the dominant parameter-efficient fine-tuning method for large language models, yet standard practice applies LoRA adapters uniformly to all transformer layers regardless of their relevance to the downstream task. We introduce Aletheia, a gradient-guided layer selection method that identifies the most task-relevant layers via a lightweight gradient probe and applies LoRA adapters only to those layers with asymmetric rank allocation. Across 81 experiment rows covering 14 successful models from 8 architecture families (0.5B-72B parameters, including dense and Mixture-of-Experts architectures), with one additional documented failed Pythia/GPT-NeoX attempt in Campaign 2, Aletheia achieves a 15-28% training speedup (mean 23.1%, p < 0.001) with bounded extra forgetting and broadly matched downstream behavior on the evaluated MMLU, GSM8K, and HumanEval benchmark pack. Across the tested families and scales, Campaign 1 shows a 100% per-model speed win rate and Campaign 2 shows broadly preserved downstream behavior within a bounded-degradation framing. Together these results support a practical model-economics claim: intelligent layer selection can make LoRA fine-tuning materially more efficient without introducing major downstream damage on the evaluated set.

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