Understanding and Guiding Layer Placement in Parameter-Efficient Fine-Tuning of Large Language Models

• URL: http://arxiv.org/abs/2602.04019v2
• Date: Sun, 08 Feb 2026 01:19:37 GMT
• Title: Understanding and Guiding Layer Placement in Parameter-Efficient Fine-Tuning of Large Language Models
• Authors: Yichen Xu, Yuyang Liang, Shan Dai, Tianyang Hu, Tsz Nam Chan, Chenhao Ma,
• Abstract summary: Large language models (LLMs) continue to grow, making parameter-efficient fine-tuning the default strategy for downstream adaptation.<n>Current practice typically applies PEFT uniformly across all layers, with limited understanding or leverage of layer selection.<n>This paper develops a unified projected residual view of PEFT on top of a frozen base model.
• Score: 19.448467763421707
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As large language models (LLMs) continue to grow, the cost of full-parameter fine-tuning has made parameter-efficient fine-tuning (PEFT) the default strategy for downstream adaptation. Constraints from inference latency in scalable serving and fine-tuning cost in edge or rapid-deployment settings make the choice of which layers to fine-tune unavoidable. Yet current practice typically applies PEFT uniformly across all layers, with limited understanding or leverage of layer selection. This paper develops a unified projected residual view of PEFT on top of a frozen base model. Under a local quadratic approximation, layerwise adaptation is governed by three quantities: (i) the projected residual norm (resnorm), which measures how much correctable bias a layer can capture; (ii) the activation energy, which determines feature conditioning; and (iii) layer coupling, which quantifies how strongly residuals interact across layers. We show that, for squared loss and linear adapters, the resnorm equals a normalized gradient norm, activation energy controls ill-conditioning and noise amplification, and weak coupling yields approximately additive layerwise contributions. Building on these insights, we introduce the Layer Card, a reusable diagnostic that summarizes residual signal strength, compute cost, and performance for each layer of a given model. With an identical model and LoRA configuration, Layer Card-guided placement refines the choice of adapted layers to flexibly prioritize different objectives, such as maximizing performance or reducing fine-tuning cost. Moreover, on Qwen3-8B, we show that selectively adapting a subset of layers can achieve performance close to full-layer LoRA while substantially reducing fine-tuning cost and the number of adapter-augmented layers during inference, offering a more cost-performance-aware alternative to full-layer insertion.

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