Learning When to Adapt

• URL: http://arxiv.org/abs/2605.19028v1
• Date: Mon, 18 May 2026 18:51:24 GMT
• Title: Learning When to Adapt
• Abstract summary: Low-rank adaptation (LoRA) is a widely used parameter-efficient fine-tuning method.<n>DiseL augments LoRA modules with lightweight input-dependent gates over individual rank-one components.<n>DiseL adds only a small number of parameters and preserves the low-rank structure.
• Score: 29.665123668818378
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Low-rank adaptation (LoRA) is a widely used parameter-efficient fine-tuning method, yet its learned correction is static: the same low-rank update is applied to every input. This input-agnostic approach creates an inevitable compromise between adapting to the fine-tuning distribution and preserving pre-trained behavior on inputs outside that distribution, contributing to catastrophic forgetting. We introduce DISeL (Dynamic Input-Sensitive LoRA), which augments LoRA modules with lightweight input-dependent gates over individual rank-one components. The gating mechanism is designed to preserve the pre-trained model's behavior by default, while training learns to activate selected components that reduce the fine-tuning loss. DISeL adds only a small number of parameters and preserves the low-rank structure. Across RoBERTa on GLUE, and Llama and Mistral models fine-tuned for mathematical reasoning and code generation, DISeL reduces forgetting relative to LoRA and related variants while maintaining competitive fine-tuning accuracy. In addition, the learned gate activations provide an interpretable diagnostic view of which layers and rank components are most activated during fine-tuning, giving insight into where task-specific adaptation is concentrated. Code available at https://github.com/alizindari/DISeL .

Related papers

• Latent-LoRA: Compact Latent-Space Adapters with Gradient-Free Routing for Continual Learning [2.749219725903528]
  Large language models generalize well to individual tasks but lack an inherent mechanism for learning them sequentially.<n>LoRA-based continual learning methods allocate a separate low-rank adapter per task, yet existing approaches either require task identity at inference or sum all adapters indiscriminately.<n>Recent gating-based solutions route inputs to the correct adapter but introduce trainable parameters that themselves need protection against forgetting.<n>Latent-LoRA is replay-free, requires no trainable routing component, and uses substantially fewer parameters per task.
  arXiv  Detail & Related papers  (2026-07-26T20:42:56Z)
• SMoA: Spectrum Modulation Adapter for Parameter-Efficient Fine-Tuning [57.85676271833619]
  Low-rank Adaptation (LoRA) uses a low-rank update method to simulate full parameter fine-tuning.<n>As the rank increases, more principal singular directions are preserved, which generally improves the model's performance.<n>We propose SMoA, a textbfSpectrum textbfModulation textbfAdapter that enlarges the accessible family of spectrum-aware updates.
  arXiv  Detail & Related papers  (2026-05-20T13:19:28Z)
• JumpLoRA: Sparse Adapters for Continual Learning in Large Language Models [78.56572636138429]
  We propose JumpLoRA, a novel framework to adaptively induce sparsity in the Low-Rank Adaptation (LoRA) blocks through the use of JumpReLU gating.<n>We demonstrate that our method is highly modular and compatible with LoRA-based CL approaches. Specifically, it significantly boosts the performance of IncLoRA and outperforms the leading state-of-the-art CL method, ELLA.
  arXiv  Detail & Related papers  (2026-04-17T15:38:37Z)
• Curvature-Guided LoRA: Steering in the pretrained NTK subspace [60.35296431630704]
  We introduce the prediction alignment problem, aiming to match the predictor obtained via PEFT to that of full fine-tuning at the level of outputs.<n>We show that this objective naturally leads to a curvature-aware, second-order formulation, where optimal low-rank updates correspond to a Newton-like, curvature-whitened gradient.<n>Based on this insight, we propose Curvature-Guided LoRA (CG-LoRA), which selects and scales adaptation directions using local curvature information.
  arXiv  Detail & Related papers  (2026-03-31T14:46:39Z)
• IGU-LoRA: Adaptive Rank Allocation via Integrated Gradients and Uncertainty-Aware Scoring [9.250460219785188]
  We introduce IGU-LoRA, an adaptive-rank LoRA that computes within-layer Integrated Gradients (IG) sensitivities and aggregates them into a layer-level score for rank allocation.<n>IGU-LoRA consistently outperforms strong PEFT baselines at matched parameter budgets, improving downstream accuracy and robustness.
  arXiv  Detail & Related papers  (2026-03-14T06:45:54Z)
• 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)
• Semantic-Guided Dynamic Sparsification for Pre-Trained Model-based Class-Incremental Learning [25.808884795490613]
  Class-Incremental Learning (CIL) requires a model to continually learn new classes without forgetting old ones.<n>We argue that this parameter-constraining method is detrimental to plasticity.<n>We propose Semantic-Guided Dynamic Sparsification (SGDS), a novel method that proactively guides the activation space.
  arXiv  Detail & Related papers  (2026-01-29T07:15:05Z)
• When Domain Pretraining Interferes with Instruction Alignment: An Empirical Study of Adapter Merging in Medical LLMs [0.6345523830122167]
  Large language models can exhibit surprising adapter interference when combining domain adaptation and instruction alignment.<n>We study a two-stage LoRA pipeline for medical LLMs, where domain-oriented pre-training (PT) and supervised fine-tuning (SFT) are trained separately and later merged.
  arXiv  Detail & Related papers  (2026-01-26T10:54:06Z)
• 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)
• 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)
• 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.