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C-LoRA: Continual Low-Rank Adaptation for Pre-trained Models

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arxiv 2502.17920 v1 pith:7MR4O4DE submitted 2025-02-25 cs.LG

Xin Zhang , Liang Bai , Xian Yang , Jiye Liang This is my paper · ORCID
classification cs.LG
keywords c-loraadaptationcontinuallearningloralow-rankparameterapproaches
verification ladder T0 review T1 audit T2 compute T3 formal
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Low-Rank Adaptation (LoRA) is an efficient fine-tuning method that has been extensively applied in areas such as natural language processing and computer vision. Existing LoRA fine-tuning approaches excel in static environments but struggle in dynamic learning due to reliance on multiple adapter modules, increasing overhead and complicating inference. We propose Continual Low-Rank Adaptation (C-LoRA), a novel extension of LoRA for continual learning. C-LoRA uses a learnable routing matrix to dynamically manage parameter updates across tasks, ensuring efficient reuse of learned subspaces while enforcing orthogonality to minimize interference and forgetting. Unlike existing approaches that require separate adapters for each task, C-LoRA enables a integrated approach for task adaptation, achieving both scalability and parameter efficiency in sequential learning scenarios. C-LoRA achieves state-of-the-art accuracy and parameter efficiency on benchmarks while providing theoretical insights into its routing matrix's role in retaining and transferring knowledge, establishing a scalable framework for continual learning.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Rethinking Transfer in Continual Learning: A Replay-Based Realisation

    cs.LG 2026-07 conditional novelty 7.0 of 10

    In continual learning, forward transfer requires target headroom, a persistent carrier, and a compatible source; routing replay by gradient signatures improves accuracy and stability over uniform replay.

  2. DeCoFlow: Structural Decomposition of Normalizing Flows for Continual Anomaly Detection

    cs.CV 2026-06 unverdicted novelty 6.5 of 10

    DeCoFlow decomposes normalizing flow subnets into frozen bases and low-rank adapters with alignment, auxiliary layers, and tail-aware loss to achieve continual anomaly detection with zero forgetting and few added parameters.

  3. ReCoLoRA: Spectrum-Aware Recursive Consolidation for Continual LLM Fine-Tuning

    cs.LG 2026-07 conditional novelty 6.0 of 10

    Spectrum-initialized LoRA with elbow ranks and recursive SVD consolidation of the effective weight beats rank-swept PEFT baselines on three of four 7–8B models in continual GLUE fine-tuning.

  4. REAL-OW: Rehearsal-free Open World Object Detection with Low-Rank Adaptation and Dual-Stage Objectness Modeling

    cs.CV 2026-07 conditional novelty 6.0 of 10

    A rehearsal-free open-world detector using collaborative LoRA adapters and dual-stage objectness modeling outperforms exemplar-replay OWOD methods on standard benchmarks.

  5. The Future of Continual Learning in the Era of Foundation Models: Three Key Directions

    cs.LG 2025-06 conditional novelty 4.0 of 10

    Continual learning should pivot from weight-update-based methods to continual compositionality and orchestration of foundation models and agents.

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