FlexLoRA: Entropy-Guided Flexible Low-Rank Adaptation

• URL: http://arxiv.org/abs/2601.22905v1
• Date: Fri, 30 Jan 2026 12:25:47 GMT
• Title: FlexLoRA: Entropy-Guided Flexible Low-Rank Adaptation
• Authors: Muqing Liu, Chongjie Si, Yuheng Jia,
• Abstract summary: Large pre-trained models achieve remarkable success across diverse domains, yet fully fine-tuning incurs prohibitive computational and memory costs.<n>We propose FlexLoRA, an entropy-guided flexible low-rank adaptation framework that supports rank pruning and expansion under a global budget.<n>Experiments show that FlexLoRA consistently outperforms state-of-the-art baselines across benchmarks.
• Score: 33.208889745659825
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large pre-trained models achieve remarkable success across diverse domains, yet fully fine-tuning incurs prohibitive computational and memory costs. Parameter-efficient fine-tuning (PEFT) has thus become a mainstream paradigm. Among them, Low-Rank Adaptation (LoRA) introduces trainable low-rank matrices and shows strong performance, nevertheless, its fixed-rank design limits flexibility. Dynamic rank allocation methods mitigate this issue by pruning redundant directions; however, they often rely on heuristic, element-level metrics that globally sort rank directions without matrix-wise distinction, and they lack mechanisms to expand capacity in layers requiring additional adaptation. To overcome these limitations, we propose FlexLoRA, an entropy-guided flexible low-rank adaptation framework that (i) evaluates matrix importance via spectral energy entropy, (ii) supports rank pruning and expansion under a global budget, and (iii) employs zero-impact initialization for newly added singular directions to ensure stability. By addressing granularity, flexibility, and stability limitations, FlexLoRA provides a more principled solution for PEFT. Extensive experiments show that FlexLoRA consistently outperforms state-of-the-art baselines across benchmarks. Codes are available at https://github.com/Chongjie-Si/Subspace-Tuning.

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