SD-LoRA: Scalable Decoupled Low-Rank Adaptation for Class Incremental Learning

• URL: http://arxiv.org/abs/2501.13198v3
• Date: Thu, 06 Mar 2025 19:43:48 GMT
• Title: SD-LoRA: Scalable Decoupled Low-Rank Adaptation for Class Incremental Learning
• Authors: Yichen Wu, Hongming Piao, Long-Kai Huang, Renzhen Wang, Wanhua Li, Hanspeter Pfister, Deyu Meng, Kede Ma, Ying Wei,
• Abstract summary: 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.
• Score: 73.93639228235622
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Continual Learning (CL) with foundation models has recently emerged as a promising paradigm to exploit abundant knowledge acquired during pre-training for tackling sequential tasks. However, existing prompt-based and Low-Rank Adaptation-based (LoRA-based) methods often require expanding a prompt/LoRA pool or retaining samples of previous tasks, which poses significant scalability challenges as the number of tasks grows. To address these limitations, 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. Our empirical and theoretical analysis reveals that SD-LoRA tends to follow a low-loss trajectory and converges to an overlapping low-loss region for all learned tasks, resulting in an excellent stability-plasticity trade-off. Building upon these insights, we introduce two variants of SD-LoRA with further improved parameter efficiency. All parameters of SD-LoRAs can be end-to-end optimized for CL objectives. Meanwhile, they support efficient inference by allowing direct evaluation with the finally trained model, obviating the need for component selection. Extensive experiments across multiple CL benchmarks and foundation models consistently validate the effectiveness of SD-LoRA. The code is available at https://github.com/WuYichen-97/SD-Lora-CL.

Related papers

• Reinforcement Learning for LLM Reasoning Under Memory Constraints [0.02488650627593658]
  We introduce S-GRPO, a memory-efficient variant of Group Relative Policy Optimization, and T-SPMO, a token-level prefix matching strategy for fine-grained credit assignment. Despite limited resources, when used to fine-tune Qwen2-1.5B both methods significantly improve SVAMP benchmark accuracy from 46% to above 70% using LoRA training. We find that our full-token GRPO baseline under LoRA fine-tuning did not improve model performance (compared to base model) on either task.
  arXiv  Detail & Related papers  (2025-04-29T14:58:43Z)
• BeamLoRA: Beam-Constraint Low-Rank Adaptation [51.52097743781401]
  Low-Rank Adaptation (LoRA) has been widely adopted as one of the most effective parameter-efficient fine-tuning methods. We propose BeamLoRA, which conceptualizes each LoRA module as a beam where each rank naturally corresponds to a potential sub-solution.
  arXiv  Detail & Related papers  (2025-02-19T10:33:22Z)
• Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs [76.40876036912537]
  Large Language Models (LLMs) demonstrate strong few-shot adaptability without requiring fine-tuning. Current Visual Foundation Models (VFMs) require explicit fine-tuning with sufficient tuning data. We propose a framework, LoRA Recycle, that distills a meta-LoRA from diverse pre-tuned LoRAs with a meta-learning objective.
  arXiv  Detail & Related papers  (2024-12-03T07:25:30Z)
• Dual Low-Rank Adaptation for Continual Learning with Pre-Trained Models [38.97142043836567]
  Continual learning (CL) aims to enable vision transformers (ViTs) to learn new tasks over time. catastrophic forgetting remains a persistent challenge. We propose a novel PEFT-CL method called Dual Low-Rank Adaptation (DualLoRA)
  arXiv  Detail & Related papers  (2024-11-01T14:28:39Z)
• 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)
• Controlled Low-Rank Adaptation with Subspace Regularization for Continued Training on Large Language Models [13.56631686493347]
  Large language models (LLMs) exhibit remarkable capabilities in natural language processing but face catastrophic forgetting when learning new tasks. We propose Controlled LoRA (CLoRA), a subspace regularization method on LoRA structure.
  arXiv  Detail & Related papers  (2024-10-22T08:27:23Z)
• Is Parameter Collision Hindering Continual Learning in LLMs? [50.57658782050275]
  Large Language Models (LLMs) often suffer from catastrophic forgetting when learning multiple tasks sequentially.<n>We show that building non-collision parameters is a more critical interdependence factor in addressing CL challenges.<n>We propose Non-collision Low-Rank Adaptation (N-LoRA), a simple yet effective approach leveraging low collision rates to enhance CL in LLMs.
  arXiv  Detail & Related papers  (2024-10-14T05:54:11Z)
• 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)
• LoRA Dropout as a Sparsity Regularizer for Overfitting Control [18.992276878667997]
  We propose a LoRA Dropout mechanism for the LoRA-based methods. We show that appropriate sparsity would help tighten the gap between empirical and generalization risks.
  arXiv  Detail & Related papers  (2024-04-15T09:32:12Z)
• PRoLoRA: Partial Rotation Empowers More Parameter-Efficient LoRA [45.38491644250814]
  Partially Rotation-enhanced Low-Rank Adaptation (PRoLoRA) is an intra-layer sharing mechanism. PRoLoRA retains its advantages, and effectively circumvents the drawbacks of peer parameter-sharing methods. Empirical experiments demonstrate the remarkably higher parameter efficiency of PRoLoRA.
  arXiv  Detail & Related papers  (2024-02-24T13:39:05Z)
• 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)
• 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)

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.