Stable-LoRA: Stabilizing Feature Learning of Low-Rank Adaptation
#Stable-LoRA #Low-Rank Adaptation #feature learning #parameter-efficient fine-tuning #model stability #AI optimization #machine learning
📌 Key Takeaways
- Stable-LoRA is a new method to improve the stability of feature learning in Low-Rank Adaptation (LoRA).
- It addresses challenges in stabilizing the training process for parameter-efficient fine-tuning of large models.
- The approach aims to enhance the reliability and performance of LoRA-based adaptations in machine learning.
- This innovation could lead to more efficient and robust fine-tuning of AI models with fewer resources.
📖 Full Retelling
arXiv:2603.05204v1 Announce Type: cross
Abstract: Low-Rank Adaptation (LoRA) is a widely adopted parameter-efficient method for fine-tuning Large Langauge Models. It updates the weight matrix as $W=W_0+sBA$, where $W_0$ is the original frozen weight, $s$ is a scaling factor and $A$,$B$ are trainable low-rank matrices. Despite its robust empirical effectiveness, the theoretical foundations of LoRA remain insufficiently understood, particularly with respect to feature learning stability. In this
🏷️ Themes
Machine Learning, Model Optimization
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Original Source
--> Computer Science > Machine Learning arXiv:2603.05204 [Submitted on 5 Mar 2026] Title: Stable-LoRA: Stabilizing Feature Learning of Low-Rank Adaptation Authors: Yize Wu , Ke Gao , Ling Li , Yanjun Wu View a PDF of the paper titled Stable-LoRA: Stabilizing Feature Learning of Low-Rank Adaptation, by Yize Wu and 3 other authors View PDF HTML Abstract: Low-Rank Adaptation is a widely adopted parameter-efficient method for fine-tuning Large Langauge Models. It updates the weight matrix as $W=W_0+sBA$, where $W_0$ is the original frozen weight, $s$ is a scaling factor and $A$,$B$ are trainable low-rank matrices. Despite its robust empirical effectiveness, the theoretical foundations of LoRA remain insufficiently understood, particularly with respect to feature learning stability. In this paper, we first establish that, LoRA can, in principle, naturally achieve and sustain stable feature learning (i.e., be self-stabilized) under appropriate hyper-parameters and initializations of $A$ and $B$. However, we also uncover a fundamental limitation that the necessary non-zero initialization of $A$ compromises self-stability, leading to suboptimal performances. To address this challenge, we propose Stable-LoRA, a weight-shrinkage optimization strategy that dynamically enhances stability of LoRA feature learning. By progressively shrinking $A$ during the earliest training steps, Stable-LoRA is both theoretically and empirically validated to effectively eliminate instability of LoRA feature learning while preserving the benefits of the non-zero start. Experiments show that Stable-LoRA consistently outperforms other baselines across diverse models and tasks, with no additional memory usage and only negligible computation overheads. The code is available at this https URL . Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2603.05204 [cs.LG] (or arXiv:2603.05204v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2603.05204 Focus to learn m...
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