LoRA-Squeeze: Simple and Effective Post-Tuning and In-Tuning Compression of LoRA Modules

• URL: http://arxiv.org/abs/2602.10993v2
• Date: Thu, 19 Feb 2026 15:57:17 GMT
• Title: LoRA-Squeeze: Simple and Effective Post-Tuning and In-Tuning Compression of LoRA Modules
• Authors: Ivan Vulić, Adam Grycner, Quentin de Laroussilhe, Jonas Pfeiffer,
• Abstract summary: We introduce LoRA-Squeeze, a simple and efficient methodology that aims to improve standard LoRA learning.<n>Our approach posits that it is better to first learn an expressive, higher-rank solution and then compress it, rather than learning a constrained, low-rank solution directly.
• Score: 10.00294036303927
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Despite its huge number of variants, standard Low-Rank Adaptation (LoRA) is still a dominant technique for parameter-efficient fine-tuning (PEFT). Nonetheless, it faces persistent challenges, including the pre-selection of an optimal rank and rank-specific hyper-parameters, as well as the deployment complexity of heterogeneous-rank modules and more sophisticated LoRA derivatives. In this work, we introduce LoRA-Squeeze, a simple and efficient methodology that aims to improve standard LoRA learning by changing LoRA module ranks either post-hoc or dynamically during training}. Our approach posits that it is better to first learn an expressive, higher-rank solution and then compress it, rather than learning a constrained, low-rank solution directly. The method involves fine-tuning with a deliberately high(er) source rank, reconstructing or efficiently approximating the reconstruction of the full weight update matrix, and then using Randomized Singular Value Decomposition (RSVD) to create a new, compressed LoRA module at a lower target rank. Extensive experiments across 13 text and 10 vision-language tasks show that post-hoc compression often produces lower-rank adapters that outperform those trained directly at the target rank, especially if a small number of fine-tuning steps at the target rank is allowed. Moreover, a gradual, in-tuning rank annealing variant of LoRA-Squeeze consistently achieves the best LoRA size-performance trade-off.

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