[2512.07703] PVeRA: Probabilistic Vector-Based Random Matrix Adaptation

Computer Science > Computer Vision and Pattern Recognition arXiv:2512.07703 (cs) [Submitted on 8 Dec 2025 (v1), last revised 30 Apr 2026 (this version, v2)] Title:PVeRA: Probabilistic Vector-Based Random Matrix Adaptation Authors:Leo Fillioux, Enzo Ferrante, Paul-Henry Cournède, Maria Vakalopoulou, Stergios Christodoulidis View a PDF of the paper titled PVeRA: Probabilistic Vector-Based Random Matrix Adaptation, by Leo Fillioux and 4 other authors View PDF Abstract:Large foundation models have emerged in the last years and are pushing performance boundaries for a variety of tasks. Training or even finetuning such models demands vast datasets and computational resources, which are often scarce and costly. Adaptation methods provide a computationally efficient solution to address these limitations by allowing such models to be finetuned on small amounts of data and computing power. This is achieved by appending new trainable modules to frozen backbones with only a fraction of the trainable parameters and fitting only these modules on novel tasks. Recently, the VeRA adapter was shown to excel in parameter-efficient adaptations by utilizing a pair of frozen random low-rank matrices shared across all layers. In this paper, we propose PVeRA, a probabilistic version of the VeRA adapter, which modifies the low-rank matrices of VeRA in a probabilistic manner. This modification naturally allows handling inherent ambiguities in the input and allows for different sampling configuratio...