持续学习

Pretraining noise in foundation models has been shown to significantly impair the generalization performance of downstream tasks, leading to catastrophic inheritance. However, when downstream tasks arrive sequentially, how pretraining noise affects continual adaptation, which involves the forgetting of previously learned knowledge, remains underexplored. This paper is the first to study this problem. Using asymmetric label noise as a realistic setting, we find that pretraining noise persistently degrades performance on new tasks during continual adaptation and may further exacerbate forgetting of previously adapted tasks. Further analysis shows that the persistent effect of pretraining noise mainly manifests as inherited output contraction, where the logit gap between the true class and competing classes is compressed. To mitigate this issue, we propose Ambiguous Boundary Correction (ABC), a method that performs anchor-guided neighborhood reweighting to correct ambiguous regions induced by inherited output contraction. Experiments on synthetic and real-world noisy pretrained models show that ABC improves robustness to pretraining noise under both domain shifts and class shifts.

Object-centric representations promise a key property for few-shot learning: Rather than treating a scene as a single unit, a model can decompose it into individual object-level parts that can be matched and compared across different concepts. In practice, this potential is rarely realized. Continual learners either collapse scenes into global embeddings, or train with part-level matching objectives that tie representations too closely to seen patterns, leaving them unable to generalize to truly novel concepts. In this paper, we identify this fundamental structural conflict and pioneer a new paradigm that strictly decouples representation learning from compositional inference. Leveraging the inherent patch-level semantic geometry of self-supervised Vision Transformers (ViTs), our framework employs a dual-phase strategy. During training, slot representations are optimized entirely toward holistic class identity, preserving highly generalizable, object-level geometries. At inference, preserved slots are dynamically composed to match novel scenes. We demonstrate that this paradigm offers dual structural benefits: The frozen backbone naturally prevents representation drift, while our lightweight, holistic optimization preserves the features' capacity for novel-concept transfer. Extensive experiments validate this approach, achieving state-of-the-art unseen-concept generalization and minimal forgetting across standard continual learning benchmarks.

As diffusion-based offline reinforcement learning (RL) move closer to real deployment, it becomes critical to remove the influence of specific training data for privacy, safety, and regulatory compliance. Since retained data may overlap with or generalize from the forget data, strict retraining equivalence can be ill-posed in offline RL. Existing unlearning methods are ineffective for diffusion policies, as training influence is dispersed across the denoising process and reinforced by critic values. We introduce Relative Fisher Forgetting (RFF), the first principled framework for selective unlearning in diffusion-based offline RL. RFF combines two asymmetric components: critic-side value suppression removes residual value incentives associated with the forget set, eliminating $Q$-guidance pathways that would otherwise sustain forgotten behaviors; actor-side relative-Fisher updates attenuate forget-set-dominant parameter influence in the denoising policy, reducing behavioral regrowth. To stabilize training, RFF alternates actor-critic updates and employs gradient clipping and retain-set regularization. Experiments on MuJoCo benchmarks show that RFF achieves the lowest identifiable forget-set reliance among baselines while preserving retained performance, and remains over $12\times$ more efficient than retraining. When undesired behaviors are primarily supported by the forget set, RFF additionally suppresses them without collateral degradation.

Graph learning research has increasingly shifted toward continual graph learning (CGL), which better reflects real-world scenarios where graphs evolve over time. However, existing CGL methods largely assume clean supervision and overlook a critical challenge: the newly arriving portions of the graph are often noisy, due to annotation errors or adversarial corruption. This mismatch limits their applicability in practice. In this work, we study robust continual graph learning, where models must simultaneously handle catastrophic forgetting and noisy supervision in evolving graph data. We show that label noise introduces a new failure mode—catastrophic remembering, where models persistently reinforce corrupted knowledge across tasks. To address these challenges, we propose a Unified Flow-Oriented framework (UFO). First, UFO models conditional feature distributions via flow-based generative modeling and produces replay representations, mitigating forgetting without storing historical data. Second, UFO estimates instance-level reliability scores to distinguish clean from noisy nodes, reducing the impact of corrupted supervision and alleviating catastrophic remembering. Extensive experiments on four benchmark graph datasets under varying noise ratios demonstrate that UFO consistently outperforms existing methods in both accuracy and forgetting metrics. Code is available at: https://anonymous.4open.science/r/UFO.

Multimodal continual learning requires MLLMs to acquire new domains and abilities sequentially while retaining previous capabilities. MoE-based methods preserve task-specific knowledge, but inference relies on reliable routing. We instead study unified continual multimodal consolidation, which forms one shared model from sequential LoRA updates and projector shifts. LoRA merging naturally supports language-side consolidation, but extending it to MLLMs raises two challenges. First, sequential LoRA updates may interfere and overwrite directions important to earlier tasks. Second, projector mismatch may make the consolidated LoRA update incompatible with the final visual-language alignment. To address these challenges, we propose **CASC** (**C**onflict-**A**ware **S**pectral **C**onsolidation), a method that jointly consolidates LoRA and projector updates. CASC maintains fixed-rank spectral banks and uses a shared protected subspace memory rule to preserve dominant historical directions while incorporating compatible new updates under fixed rank budgets, yielding a single model without replay data or inference time routing. Both theoretical analysis and experimental results demonstrate the effectiveness of CASC in addressing multimodal continual learning.

Continual learning aims to acquire tasks sequentially without catastrophic forgetting, yet standard strategies face a core tradeoff: regularization-based methods (e.g., EWC) can overconstrain updates when task optima are weakly overlapping, while replay-based methods can retain performance but drift due to imperfect replay. We study a hybrid perspective: \emph{trust region continual learning} that combines generative replay with a Fisher-metric trust region constraint. We show that, under local approximations, the resulting update admits a MAML-style interpretation with a single implicit inner step: replay supplies an old-task gradient signal (query-like), while the Fisher-weighted penalty provides an efficient offline curvature shaping (support-like). This yields an emergent meta-learning property in continual learning: the model becomes an initialization that rapidly \emph{re-converges} to prior task optima after each task transition, without explicitly optimizing a bilevel objective. Empirically, on task-incremental diffusion image generation and continual diffusion-policy control, trust region continual learning achieves the best final performance and retention, and consistently recovers early-task performance faster than EWC, replay, and continual meta-learning baselines.

Lifelong person re-identification (LReID) requires models to continuously learn from sequentially arriving domains while retaining discriminative power for previously seen identities. A key challenge is to prevent catastrophic forgetting without access to old data, especially under exemplar-free constraints. While flat-minima optimization has shown promise in continual learning, we identify a gradient conflict that limits the effectiveness of standard Sharpness-Aware Minimization (SAM) in LReID. The ReID loss gradient dominates the perturbation direction, causing the sharpness of the distillation loss to be underestimated, which hinders the flattening of the landscape necessary for knowledge retention. To resolve this conflict, we propose a framework that unifies selective flatness-aware optimization, dual-model training, and weight-space interpolation. Specifically, we maintain two models per task: a stability model whose SAM perturbation is computed solely from the distillation loss, and a plasticity model optimized for the current domain. By decoupling the perturbation objectives, our selective SAM achieves more targeted sharpness exploration along the distillation landscape, guiding the stability model toward flatter and robust regions. After training, the two models are fused via weight-space interpolation, and we provide a theoretical bound showing that flatter stability solutions tighten the interpolation bound and reduce forgetting during fusion. Our method is lightweight, modular, and readily compatible with existing LReID frameworks. Experimental results demonstrate that the proposed method consistently improves the performance of LReID in terms of both knowledge retention and generalization to unseen domains.

Exemplar-Free Class-Incremental Learning (EFCIL) is a challenging continual learning paradigm where a model must learn new classes sequentially without access to old data, making it susceptible to catastrophic forgetting. The core difficulty lies in balancing stability (preserving old knowledge) and plasticity (acquiring new knowledge). We propose Subspace-Guided Continual Learning (SGCL), a novel method that tackles this dilemma from a geometric perspective. SGCL decomposes the feature space into two orthogonal subspaces: a ```stable subspace``` containing directions critical for previous tasks, and a ```plastic subspace``` where new knowledge can be learned with minimal interference. This decomposition is efficiently identified via the feature-space Hessian, where high-curvature eigendirections define the stable subspace. Building on this, SGCL introduces two synergistic components: 1) Subspace-Guided Regularization (SGR), which imposes curvature-weighted penalties on feature drifts within the stable subspace, and 2) Subspace-Guided Prototype Alignment (SGPA), which adaptively corrects the shift of old-class prototypes to recalibrate the classifier. Extensive experiments on standard benchmarks show that SGCL consistently achieves competitive or superior performance compared to existing state-of-the-art methods, offering a principled approach to mitigating forgetting through loss landscape analysis.

Model merging enables the integration of multiple expert models with different capabilities into a unified model. In practical deployments, new expert models are often continuously updated and arriving. Meanwhile, due to the training and communication overhead of large language models (LLMs) and multimodal large language models (MLLMs), Low-Rank Adaptation (LoRA) is widely used to adapt large models to specific domains due to its efficiency. This scenario brings a new challenge: continual LoRA merging of deployed MLLMs to load new task capabilities. Naive merging faces catastrophic forgetting and parameter conflicts. We first observe the distinct features of the LoRA direction and layers in MLLM LoRA tuning. Based on this, we propose our Spectral Rank Calibration Merging (SRC-Merging), a data-free and train-free continual LoRA merging method tailored for MLLMs. SRC-Merging regards the deployed LoRA as a compressed spectral memory and performs calibrated rank-level merging for each incoming adapter. It aims to preserve accumulated multimodal knowledge while avoiding over-suppression of new vision-language task updates during continual merging. Extensive experiments on multiple multimodal tasks have demonstrated that our method significantly outperforms state-of-the-art merging methods.

Continual learning suffers from catastrophic forgetting, leading to the loss of past knowledge and unstable adaptation across task streams. Existing approaches typically address this issue through replay buffers, architectural changes, or additional regularization terms. We argue that forgetting is a consequence of unstable learning dynamics, which may benefit from an optimizer-level solution. The main challenge is that learning dynamics are time-dependent, as demonstrated by empirical phenomena such as the stability gap. First, we formally prove that, for distance-based classifiers, bounded parameter drift guarantees bounded margin erosion under a local score-stability condition. Based on this insight, we propose STABLE, a primal–dual optimizer with adaptive drift control. STABLE enforces a drift budget by increasing the dual variable when the budget is violated and decreasing it otherwise. This results in high stability without penalizing plasticity. Experiments on long-horizon unimodal streams and CLIP-based multimodal streams show that vanilla PEFT baselines trained with STABLE match or outperform specialized continual learning methods. In the multimodal setting, STABLE further preserves zero-shot transfer, matching or exceeding dedicated multimodal continual learning methods.

In continual learning, most parameter-efficient fine-tuning methods rely on task-specific low-rank adapters to incrementally adapt pre-trained models to new tasks. However, they overlook two critical challenges: one is the accumulation of numerous adapters that leads to linear growth in parameters and memory, and the other is the lack of an explicit mechanism to preserve previously learned knowledge during continual adaptation. In this work, we propose a simple yet scalable framework, termed Progressive Merging of Low-Rank Adapters (PM-LoRA), to address both issues simultaneously. Specifically, PM-LoRA trains a small task-specific spectral orthogonal adapter for each new task and progressively integrates it into a single evolving LoRA adapter that encodes all prior knowledge. This merging process enables continuous knowledge integration while directly mitigating catastrophic forgetting at the parameter level. Benefiting from this design, PM-LoRA achieves true scalability by maintaining constant parameter overhead throughout continual training. Extensive experiments on four benchmark datasets demonstrate that PM-LoRA achieves state-of-the-art performance with superior memory and computation efficiency.

Reinforcement Fine-Tuning (RFT) with verifiable rewards has emerged as a powerful learning paradigm for eliciting reasoning capabilities in Multi-modal Large Language Models (MLLMs). Recent studies also suggest that RFT (e.g., GRPO) is inherently more resilient to catastrophic forgetting than Supervised Fine-Tuning (SFT). However, whether RFT can effectively overcome forgetting in challenging visual continual learning settings, such as class-incremental learning (CIL) and domain-incremental learning (DIL), remains an open problem. Through a pilot study on rehearsal-free CIL, we confirm that while RFT consistently outperforms SFT, it still suffers from non-negligible forgetting. We empirically trace this bottleneck to Trajectory-level Drift Agnosticism: among candidate rollouts achieving identical task rewards, the KL divergence from the preceding-task policy varies substantially, which strongly correlates with catastrophic forgetting across sequential tasks. Motivated by this insight, we propose Retention-aware Policy Optimization (RaPO), a simple yet effective RFT framework that explicitly mitigates forgetting through trajectory-level reward shaping. Specifically, RaPO comprises two core components: (1) Retention Reward that converts trajectory-level distribution drift into a continuous reward signal, preferentially reinforcing knowledge-preserving rollouts within each group; (2) Cross-Task Advantage Normalization (CTAN), which maintains a persistent exponential moving average of reward statistics across task boundaries to stabilize the optimization progress during continual learning. Leveraging the free-form textual generalization of MLLMs, we comprehensively evaluate RaPO across five visual continual learning settings, spanning class- and domain-incremental image classification, class-incremental video classification, and class- and domain-incremental object detection. Extensive experiments demonstrate that RaPO achieves leading performance, substantially reducing catastrophic forgetting while preserving strong plasticity. To the best of our knowledge, this work represents the first systematic exploration of RFT in visual continual learning, offering insights that we hope will inspire future research.

Catastrophic forgetting is a central obstacle in single-stage fine-tuning of large language models (LLMs) on a new task, especially when the original pretraining data are unavailable. Synthetic data offers a practical mitigation path, but candidate pools are often noisy and redundant, making effective selection essential. Existing methods often rely on indirect proxy signals whose alignment with actual training benefit can be limited, while utility--diversity selection over large candidate pools remains computationally demanding. To address these challenges, we propose $\textbf{NA}$vigator-guided $\textbf{D}$ata $\textbf{S}$election ($\textbf{NADS}$), a fine-tuning framework for LLMs. NADS first follows the target fine-tuning recipe without preservation regularization to obtain a navigator model, whose deviation from the pretrained model exposes recipe-induced capability drift. It then uses the predictive divergence between the navigator and the pretrained model to score forgetting-aware utility for each synthetic candidate, and constructs a constraint set through an efficient utility--diversity selection strategy. Finally, NADS fine-tunes on the new-task data while distilling pretrained behavior on the selected constraints to preserve general capabilities. Experiments show that NADS delivers a stronger balance between new-task performance and general capability preservation, while reducing utility--diversity selection overhead.

Continual learning (CL) aims to adapt models to new tasks while preserving previously learned knowledge. Recently, Low-Rank Adaptation (LoRA), a representative Parameter-Efficient Fine-Tuning (PEFT) method, has gained increasing attention in CL due to its efficiency and scalability. Several LoRA-based merging methods maintain a single inference model by scaling the newly learned LoRA update with one coefficient and merging it into the previous weights after each task. However, they usually control the whole update with a shared scalar factor, although different update elements may affect previous and current tasks in different ways. To address this issue, we propose Merging Strength Modulation via Joint Loss Estimation (M²LE) It consists of Hessian Information-guided Adaptive Merging (HIM) and Task Simulation Perturbation for Merging (TSP). Instead of searching for a global merge coefficient, HIM directly solves for the target merged update under a joint objective over previous and current tasks. Solving this objective gives an analytic solution that naturally takes the form of element-wise modulation over the current task LoRA update. To improve the stability of the learned update under subsequent merging, we introduce TSP which simulates different tasks interference. Experiments on multiple benchmarks show that M²LE consistently outperforms existing PEFT-based continual learning methods. Our code is provided in the supplementary material.

Continual learning requires models to adapt to new data while preserving previously acquired knowledge. At its core, this challenge can be viewed as principled one-step adaptation: incorporating new information with minimal interference to existing representations. Most existing approaches address this challenge by modifying model parameters or architectures in a supervised, task-specific manner. However, the underlying issue is representational: tasks require distinct yet structured representations that can be selectively updated without disrupting representations, while structure should reflect intrinsic organization in the data rather than task boundaries. In sequential data, time-delayed dependencies provide a natural signal for uncovering this organization, revealing how fundamental representations give rise to more specific ones. Inspired by the modular organization of the human brain, we propose MoRe, a framework that identifies modularity in the representation itself rather than allocating it at the architectural level. MoRe decomposes knowledge into a hierarchy of fundamental and specific modules with identifiability guarantees, enabling principled module reuse, alignment, and expansion during adaptation while preserving old modules by construction. Experiments on synthetic benchmarks and real-world LLM activations demonstrate interpretable hierarchical structure, improved plasticity-stability trade-offs, suggesting MoRe as a principled foundation for continual adaptation.

When learning new concepts sequentially, Vision-Language Models (VLMs) face forgetting that degrades both previously acquired tasks and inherent zero-shot capabilities. This challenge is particularly pronounced in the Cross-domain Task-Agnostic Incremental Learning (X-TAIL) setting, where the absence of explicit task identifiers forces diverse concepts to coexist within a unified and crowded multi-modal space. In this context, we identify an underexplored vulnerability termed Asymmetric Boundary Encroachment (ABE). Even when historical feature spaces remain stable, unconstrained new text features actively encroach upon these established sub-spaces during training. To systematically counteract ABE, we propose a unified framework. First, we introduce Analytic Decision Boundary (ADB), which constructs a geometric defense by enforcing an analytic margin derived from cumulative statistics to secure historical multi-modal boundaries. Furthermore, we integrate Orthogonal Visual Adaptation (OVA) and Analytic Ridge Classifier (ARC) to safely evolve the visual backbone and enhance joint inference. Experiments under X-TAIL setting demonstrate that our framework effectively mitigates ABE and consistently achieves new state-of-the-art performance. Our code is available at https://anonymous.4open.science/r/abe-cl-vlm.

Continual learning studies how models can adapt to new tasks while retaining previously acquired knowledge. Although a broad spectrum of methods has been proposed to mitigate catastrophic forgetting, the field remains predominantly performance-driven, with limited insight into what forgetting actually corresponds to within the vision model's representation space. Prior work has primarily analyzed forgetting through task-level performance or coarse measures of representational drift, without disentangling output-level accessibility from changes in finer-grained internal structure.To this end, we propose a diagnostic framework that leverages Sparse Autoencoders (SAEs) to define a task-anchored latent feature space, enabling analysis of how task-specific information evolves at a finer granularity, where individual SAE latents are treated as concept proxies for recurring and relatively disentangled visual patterns in the model’s internal computations. Within this framework, we decompose forgetting into apparent concept deletion, recoverability, and decodability. We show that a large portion of seemingly lost concept-level information can often be recovered under linearity assumption, with concept decodability degrading as more tasks are introduced. Overall, our findings suggest that a significant part of concept-level forgetting can be attributed to changes in the representational accessibility rather than complete information erasure.

Continual learning seeks to acquire new knowledge while preserving previously learned representations. Despite the success of prompt-based methods, they remain fragile in domain-incremental learning scenarios, where shifts in the input distribution cause representation drift in shared layers and lead to forgetting. We introduce InTAct, a method that preserves functional behavior in shared representations without freezing parameters or retaining past data. InTAct constrains updates within activation regions associated with prior domains, while allowing flexible adaptation elsewhere, thereby stabilizing neuron functionality rather than directly restricting parameter values. The approach is architecture-agnostic, integrates seamlessly with existing prompt-based frameworks, and matches or outperforms state-of-the-art methods on domain-incremental benchmarks.

Effective Continual Video Instance Segmentation (CVIS) requires balancing catastrophic forgetting with the plasticity to learn new tasks. Existing methods typically adapt pre-trained VIS models using trainable visual prompts, but suffer from two key limitations: they overlook the distinct degradation of frame-level spatial knowledge (e.g., misclassification) and video-level temporal knowledge (e.g., trajectory disruption), and rely on rigid prompt retrieval that struggles under task ambiguity and distribution shifts. To address these issues, we propose STEAM, a CVIS framework with Spatial-Temporal Balanced Mixture-of-Experts Adapters. Instead of explicit retrieval, we introduce a Distribution-Aware spatial-temporal Scaling and sHifting (DASH) mechanism to align the pre-trained feature space with sequential tasks and reduce the domain gap. Furthermore, our method adaptively learns Task-Adaptive Spatial-Temporal mixture-of-Experts (TASTE) at both the frame and video levels, while updating them with Subspace-Constrained Orthogonal Adaptation (SCOA) to mitigate interference. Extensive experiments across diverse CVIS settings show that STEAM preserves temporal-spatial coherence and achieves state-of-the-art performance.

Multimodal large language models must continually adapt to evolving tasks and domains, yet standard continual learning metrics mainly measure whether old answers remain correct, leaving the stability of multimodal grounding largely unexamined. We study this overlooked failure mode and ask whether a continually adapted MLLM can preserve not only what it answers, but also how it uses visual, textual, OCR, chart, and document evidence. We identify hidden evidence-use forgetting, where answer accuracy is retained while the model silently shifts toward different or less grounded evidence channels, and propose RCL, a replay-free reliance-constrained continual learning framework. RCL freezes the previous checkpoint as a behavioral reference, estimates teacher and student evidence-reliance profiles through counterfactual channel interventions, and jointly optimizes task learning, prediction preservation, and reliance preservation without adding inference-time cost. Across CoIN, COAST, MCITlib, and an evidence-sensitive multimodal stream, RCL consistently improves final performance and reduces forgetting over replay-free, PEFT, routing, and memory-assisted baselines, while substantially lowering modality reliance drift, dominant evidence flips, and hidden forgetting rates. These results suggest that robust continual multimodal learning requires preserving the evidence path behind correct answers, not merely the answers themselves.