IFCLoRA: Topology-Aware Rank Allocation for Parameter-Efficient Fine-Tuning
Low-Rank Adaptation (LoRA) is a widely used approach to parameter-efficient fine-tuning (PEFT) of LLMs whose effectiveness depends on rank allocation. Existing adaptive LoRA methods derive ranks from local gradient, activation, or matrix statistics collected before or during fine-tuning; training-time variants add overhead, and local signals reveal little about each module's structural role in information propagation, giving weak global grounding for scarce-capacity allocation. We propose IFCLoRA, a topology-aware method for pre-fine-tuning rank allocation and adapter initialization. Using a small calibration set, IFCLoRA performs intervention tracing on the frozen model and constructs a ...
| Authors | Wei Zhang, Xinwu Liu, Yihang Cheng |
|---|---|
| Primary category | cs.LG |
| Published | 24 July 2026 |
| Last updated at source | 7 August 2026 |
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