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arXiv · 2606.28117

When One Adapter Speaks for Many: Discovering Low-Rank Redundancy in Continual Fine-Tuning

Tanguy Dieudonné·Giulia Lanzillotta·Enis Simsar·Louis Barinka·Thomas Hofmann
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Abstract

Low-Rank Adaptation (LoRA) has become the standard tool for parameter-efficient fine-tuning of large pretrained models. When applied sequentially across tasks in Continual Learning (CL), the standard assumption is that each new task requires a dedicated low-rank adapter. In this work, we challenge this assumption empirically and structurally. We show that task-specific LoRA adapters in CL exhibit significant low-rank redundancy: the subspaces spanned by adapters trained on different tasks substantially overlap, and in many cases earlier adapters can faithfully represent later tasks. Building on this observation, we propose LiteLoRA, a plug-and-play gating mechanism that learns at train time whether to recruit a new adapter or reuse existing low-rank representations. Our method reduces the number of active adapters by 20-70% while matching or exceeding state-of-the-art performance on standard CL benchmarks, revealing that structural redundancy is pervasive and that selective learning is sufficient to achieve stability without sacrificing plasticity.

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Tanguy Dieudonné, Giulia Lanzillotta, Enis Simsar, Louis Barinka, Thomas Hofmann. 2026-06-26. When One Adapter Speaks for Many: Discovering Low-Rank Redundancy in Continual Fine-Tuning. https://arxiv.org/abs/2606.28117

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