VD-LoRA: Adaptive Reuse of Low-Rank Directions for Continual Learning

Class-incremental learning (CIL) aims to continually acquirenew classes while preserving previously learned knowledge, posing thewell-known stability–plasticity dilemma. With the emergence of largepre-trained models, parameter-efficient fine-tuning methods such as LoRAhave become a practical paradigm for rehearsal-free CIL by restrictingadaptation to low-rank weight updates. Recent LoRA-based approachesoften mitigate cross-task interference by enforcing orthogonality betweentask updates. However, under strict rank budgets and long task se-quences, uniformly protecting previously used directions can becomeoverly conservative, progressively shrinking the effective update spaceand limiting plasticity. To address this limitation, we propose Varia-tional Direction-aware LoRA (VD-LoRA), a Bayesian framework thatenables adaptive consolidation within the low-rank update space. VD-LoRA maintains a recursive variational posterior over LoRA directionsto estimate direction-wise uncertainty, using posterior precision to adap-tively control protection strength. Based on this uncertainty signal, wefurther design a direction selection mechanism that favors updates thatare both task-relevant and safe to adapt. This enables selective reuseof low-risk directions while preserving critical ones, maintaining a task-aligned update subspace under fixed rank constraints. Extensive experi-ments on multiple rehearsal-free CIL benchmarks demonstrate that VD-LoRA consistently outperforms existing LoRA-based methods while im-proving long-horizon stability and plasticity. Our code is available at