Dual LoRA: Enhancing LoRA with Magnitude and Direction Updates

• URL: http://arxiv.org/abs/2512.03402v1
• Date: Wed, 03 Dec 2025 03:14:09 GMT
• Title: Dual LoRA: Enhancing LoRA with Magnitude and Direction Updates
• Authors: Yixing Xu, Chao Li, Xuanwu Yin, Spandan Tiwari, Dong Li, Ashish Sirasao, Emad Barsoum,
• Abstract summary: Low-rank adaptation (LoRA) is one of the most popular methods among parameter-efficient fine-tuning (PEFT)<n>We propose a novel method called Dual LoRA to improve the performance by incorporating an inductive bias into the original LoRA.<n>We show that we consistently outperform LoRA and its state-of-the-art variants with the same number of trainable parameters.
• Score: 14.49537642990529
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Low-rank adaptation (LoRA) is one of the most popular methods among parameter-efficient fine-tuning (PEFT) methods to adapt pre-trained large language models (LLMs) to specific downstream tasks. However, the model trained based on LoRA often has an unsatisfactory performance due to its low-rank assumption. In this paper, we propose a novel method called Dual LoRA to improve the performance by incorporating an inductive bias into the original LoRA. Specifically, we separate low-rank matrices into two groups: the magnitude group to control whether or not and how far we should update a parameter and the direction group to decide whether this parameter should move forward or backward, to better simulate the parameter updating process of the full fine-tuning based on gradient-based optimization algorithms. We show that this can be simply achieved by adding a ReLU function to the magnitude group and a sign function to the direction group. We conduct several experiments over a wide range of NLP tasks, including natural language generation (NLG), understanding (NLU), and commonsense reasoning datasets on GPT-2, RoBERTa, DeBERTa, and LLaMA-1/2/3 as baseline models. The results show that we consistently outperform LoRA and its state-of-the-art variants with the same number of trainable parameters.

Related papers

• Beyond SGD, Without SVD: Proximal Subspace Iteration LoRA with Diagonal Fractional K-FAC [50.36542772932594]
  Low-Rank Adaptation (LoRA) fine-tunes large models by learning low-rank updates on top of frozen weights.<n>In this work, we address the gap between training with full steps with low-rank projections (SVDLoRA) and LoRA fine-tuning.<n>We propose LoRSum, a memory-efficient subroutine that closes this gap for gradient descent.
  arXiv  Detail & Related papers  (2026-02-18T13:41:41Z)
• Faster Than SVD, Smarter Than SGD: The OPLoRA Alternating Update [50.36542772932594]
  Low-Rank Adaptation (LoRA) fine-tunes large models by learning low-rank updates on top of frozen weights.<n>There is still a gap between full training with low-rank projections (SVDLoRA) and LoRA fine-tuning, indicating that LoRA steps can be further improved.
  arXiv  Detail & Related papers  (2025-09-24T10:32:50Z)
• DropLoRA: Sparse Low-Rank Adaptation for Parameter-Efficient Fine-Tuning [5.103108721904429]
  We introduce DropLoRA, a novel pruning-based approach that focuses on pruning the rank dimension.<n>By continuously adapting the learning subspace, DropLoRA significantly boosts performance without incurring additional training or infer- ence costs.
  arXiv  Detail & Related papers  (2025-08-24T12:45:36Z)
• Uni-LoRA: One Vector is All You Need [21.893406288629734]
  Low-Rank Adaptation (LoRA) has become the de facto parameter-efficient fine-tuning (PEFT) method for large language models.<n>Recent works such as Tied-LoRA, VeRA, and VB-LoRA push efficiency further by introducing additional constraints to reduce the trainable parameter space.<n>We show that the parameter space reduction strategies employed by these LoRA variants can be formulated within a unified framework, Uni-LoRA.
  arXiv  Detail & Related papers  (2025-06-01T03:00:09Z)
• LoRA Done RITE: Robust Invariant Transformation Equilibration for LoRA Optimization [78.93425154518705]
  Low-rank adaption (LoRA) is a widely used parameter-efficient finetuning method for LLM that reduces memory requirements.<n>This paper introduces LoRA-RITE, a novel adaptive matrix preconditioning method for LoRA optimization.
  arXiv  Detail & Related papers  (2024-10-27T22:57:12Z)
• Less is More: Extreme Gradient Boost Rank-1 Adaption for Efficient Finetuning of LLMs [75.11449420928139]
  Fine-tuning Large Language Models (LLMs) has become a crucial technique for adapting pre-trained models to downstream tasks. Low-Rank Adaptation (LoRA) has emerged as a promising solution, but there exists a gap between the practical performance of low-rank adaptations and its theoretical optimum. We propose eXtreme Gradient Boosting LoRA, a novel framework that bridges this gap by leveraging the power of ensemble learning.
  arXiv  Detail & Related papers  (2024-10-25T17:07:13Z)
• Randomized Asymmetric Chain of LoRA: The First Meaningful Theoretical Framework for Low-Rank Adaptation [58.288682735160585]
  Low-Rank Adaptation (LoRA) is a popular technique for finetuning models. LoRA often under performs when compared to full- parameter fine-tuning. We present a framework that rigorously analyzes the adaptation rates of LoRA methods.
  arXiv  Detail & Related papers  (2024-10-10T18:51:53Z)
• ALoRA: Allocating Low-Rank Adaptation for Fine-tuning Large Language Models [8.251547772610301]
  We extend the methodology of low-rank adaptation (LoRA) to an innovative approach we call allocating low-rank adaptation (ALoRA) First, we propose a novel method, AB-LoRA, that can effectively estimate the importance score of each LoRA rank. Second, guided by AB-LoRA, we gradually prune abundant and negatively impacting LoRA ranks and allocate the pruned LoRA budgets to important Transformer modules needing higher ranks.
  arXiv  Detail & Related papers  (2024-03-24T15:09:55Z)
• Chain of LoRA: Efficient Fine-tuning of Language Models via Residual Learning [31.036465632204663]
  We introduce Chain of LoRA, an iterative optimization framework inspired by the Frank-Wolfe algorithm. We demonstrate that COLA can consistently outperform LoRA without additional computational or memory costs.
  arXiv  Detail & Related papers  (2024-01-08T14:26:49Z)
• Sparse Low-rank Adaptation of Pre-trained Language Models [79.74094517030035]
  We introduce sparse low-rank adaptation (SoRA) that enables dynamic adjustments to the intrinsic rank during the adaptation process. Our approach strengthens the representation power of LoRA by initializing it with a higher rank, while efficiently taming a temporarily increased number of parameters. Our experimental results demonstrate that SoRA can outperform other baselines even with 70% retained parameters and 70% training time.
  arXiv  Detail & Related papers  (2023-11-20T11:56:25Z)
• NOLA: Compressing LoRA using Linear Combination of Random Basis [22.76088132446952]
  We introduce NOLA, which overcomes the rank one lower bound present in LoRA. NOLA performs as well as LoRA models with much fewer number of parameters compared to LoRA with rank one, the best compression LoRA can archive.
  arXiv  Detail & Related papers  (2023-10-04T03:30:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.