TLoRA: Task-aware Low Rank Adaptation of Large Language Models

• URL: http://arxiv.org/abs/2604.18124v1
• Date: Mon, 20 Apr 2026 11:43:55 GMT
• Title: TLoRA: Task-aware Low Rank Adaptation of Large Language Models
• Abstract summary: Low-Rank Adaptation (LoRA) has become a widely adopted parameter-efficient fine-tuning method for large language models.<n>Existing LoRA variants address only one of these factors, often at the cost of increased training complexity or reduced practical efficiency.<n>We present Task-aware Low-Rank Adaptation (TLoRA), a unified framework that jointly optimalizes and resource allocation at the outset of training.
• Score: 8.568676581408239
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Low-Rank Adaptation (LoRA) has become a widely adopted parameter-efficient fine-tuning method for large language models, with its effectiveness largely influenced by the allocation of ranks and scaling factors, as well as initialization. Existing LoRA variants typically address only one of these factors, often at the cost of increased training complexity or reduced practical efficiency. In this work, we present Task-aware Low-Rank Adaptation (TLoRA), a unified framework that jointly optimizes initialization and resource allocation at the outset of training. TLoRA introduces a data-driven initialization strategy that aligns the LoRA $A$ matrix with task-relevant subspaces by performing singular value decomposition on the product of pre-trained weights and input activation covariance. After this, the $A$ matrix is frozen, and only the $B$ matrix is trained. Furthermore, TLoRA employs a sensitivity-based importance metric to adaptively allocate ranks and scaling factors across layers under a fixed parameter budget. We conduct extensive experiments that demonstrate TLoRA consistently performs excellently across various tasks, including natural language understanding, commonsense reasoning, math reasoning, code generation, and chat generation, while significantly reducing the number of trainable parameters.

Related papers

• Decomposing and Composing: Towards Efficient Vision-Language Continual Learning via Rank-1 Expert Pool in a Single LoRA [50.97792275353563]
  We introduce a novel framework that restructures a single Low-Rank Adaptation (LoRA) module as a decomposable Rank-1 Expert Pool.<n>Our method learns to dynamically compose a sparse, task-specific update by selecting from this expert pool, guided by the semantics of the [Guided] token.
  arXiv  Detail & Related papers  (2026-01-30T10:54:51Z)
• Less is More: Resource-Efficient Low-Rank Adaptation [15.883867662707743]
  EffiLoRA is a lightweight and generalizable approach for language, multimodal, and diffusion models.<n>It consistently outperforms LoRA across diverse modalities, including commonsense reasoning, visual instruction tuning, and image generation.
  arXiv  Detail & Related papers  (2025-11-30T12:52:04Z)
• ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints [64.35580479051208]
  In previous works, low-rank adapters (LoRA) are randomly with a fixed rank across all attachment points.<n>In this paper, we improve convergence and final performance of LoRA fine-tuning using our proposed data-driven weight initialization method.
  arXiv  Detail & Related papers  (2025-07-09T23:52:31Z)
• OSoRA: Output-Dimension and Singular-Value Initialized Low-Rank Adaptation [9.048461365342204]
  We present OSoRA, a novel PEFT method for Large Language Models (LLMs)<n>OSoRA substantially reduces computational resource requirements by minimizing the number of trainable parameters during fine-tuning.<n> Comprehensive evaluations across mathematical reasoning, common sense reasoning, and other benchmarks demonstrate that OSoRA achieves comparable or superior performance to state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-05-20T13:34:06Z)
• SD-LoRA: Scalable Decoupled Low-Rank Adaptation for Class Incremental Learning [73.93639228235622]
  Continual Learning with foundation models has emerged as a promising paradigm to exploit abundant knowledge acquired during pre-training for tackling sequential tasks.<n>Existing prompt-based and Low-Rank Adaptation-based (LoRA-based) methods often require expanding a prompt/LoRA pool or retaining samples of previous tasks.<n>We propose Scalable Decoupled LoRA (SD-LoRA) for class incremental learning, which continually separates the learning of the magnitude and direction of LoRA components without rehearsal.
  arXiv  Detail & Related papers  (2025-01-22T20:00:41Z)
• GeLoRA: Geometric Adaptive Ranks For Efficient LoRA Fine-tuning [2.7446241148152253]
  Fine-tuning large language models (LLMs) is computationally intensive because it requires updating all parameters.<n>Low-Rank Adaptation (LoRA) improves efficiency by modifying only a subset of weights but introduces a trade-off between expressivity and computational cost.<n>We propose Geometric Low-Rank Adaptation (GeLoRA), a novel framework that computes the intrinsic dimensionality of hidden state representations to adaptively select LoRA ranks.
  arXiv  Detail & Related papers  (2024-12-12T13:04:54Z)
• 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)
• CoRA: Optimizing Low-Rank Adaptation with Common Subspace of Large Language Models [7.108651381160281]
  Low-Rank Adaptation (LoRA) strategy balances efficiency and performance in fine-tuning large models. We propose textbfCoRA: leveraging shared knowledge to optimize LoRA training by substituting its matrix $B$ with a common subspace from large models. Our experiments show that the first approach achieves the same efficacy as the original LoRA fine-tuning while being more efficient than halving parameters.
  arXiv  Detail & Related papers  (2024-08-31T12:48:27Z)
• OLoRA: Orthonormal Low-Rank Adaptation of Large Language Models [0.0]
  Low-Rank Adaptation (LoRA) has emerged as a promising method to mitigate these issues. OLoRA significantly accelerates the convergence of LLM training. OLoRA exhibits improved performance compared to standard LoRA across a variety of language modeling tasks.
  arXiv  Detail & Related papers  (2024-06-03T20:37:27Z)
• PRILoRA: Pruned and Rank-Increasing Low-Rank Adaptation [65.268245109828]
  We introduce PRILoRA, which linearly allocates a different rank for each layer, in an increasing manner, and performs pruning throughout the training process. We validate the effectiveness of PRILoRA through extensive experiments on eight GLUE benchmarks, setting a new state of the art.
  arXiv  Detail & Related papers  (2024-01-20T20:25:17Z)
• AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning [143.23123791557245]
  Fine-tuning large pre-trained language models on downstream tasks has become an important paradigm in NLP. We propose AdaLoRA, which adaptively allocates the parameter budget among weight matrices according to their importance score. We conduct extensive experiments with several pre-trained models on natural language processing, question answering, and natural language generation to validate the effectiveness of AdaLoRA.
  arXiv  Detail & Related papers  (2023-03-18T22:36:25Z)

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.