Post-Optimization Adaptive Rank Allocation for LoRA

• URL: http://arxiv.org/abs/2604.27796v1
• Date: Thu, 30 Apr 2026 12:40:11 GMT
• Title: Post-Optimization Adaptive Rank Allocation for LoRA
• Abstract summary: Post-Optimization Adaptive Rank Allocation (PARA) is a data-free compression method for Low-Rank Adaptation (LoRA)<n>We propose PARA, a data-free compression method for LoRA that integrates seamlessly into existing fine-tuning pipelines.<n>We empirically demonstrate that PARA reduces parameter count by 75-90% while preserving the predictive performance of the original, uncompressed LoRA across vision and language benchmarks.
• Score: 10.796887002907456
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Exponential growth in the scale of modern foundation models has led to the widespread adoption of Low-Rank Adaptation (LoRA) as a parameter-efficient fine-tuning technique. However, standard LoRA implementations disregard the varying intrinsic dimensionality of model layers and enforce a uniform rank, leading to parameter redundancy. We propose Post-Optimization Adaptive Rank Allocation (PARA), a data-free compression method for LoRA that integrates seamlessly into existing fine-tuning pipelines. PARA leverages Singular Value Decomposition to prune LoRA ranks using a global threshold over singular values across all layers. This results in non-uniform rank allocation based on layer-wise spectral importance. As a post-hoc method, PARA circumvents the training modifications and resulting instabilities that dynamic architectures typically incur. We empirically demonstrate that PARA reduces parameter count by 75-90\% while preserving the predictive performance of the original, uncompressed LoRA across multiple vision and language benchmarks. Code will be published upon acceptance.

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