The paper addresses the conflict between generalization and continuous adaptation in few‑shot and continual learning. It proposes a Complementary Learning System (CLS) that separates a fast Short‑Term Memory (STM) for memorizing recent inputs from a slow Long‑Term Memory (LTM) that provides robust, generalized perceptions. Experiments on image classification show the CLS model resists forgetting in domain‑incremental continual learning and outperforms other continual learners in few‑shot settings, though overall accuracy is slightly lower than some existing methods.
arXiv:2606. 03843v1 Announce Type: cross Abstract: Continual learning methods aim to maximize the stability and plasticity of machine learning models that are trained on a sequence of tasks.
arXiv:2607. 07847v1 Announce Type: new Abstract: As large language models (LLMs) become increasingly capable, the next question is how can we enable models to continually learn?
arXiv:2607. 04364v1 Announce Type: new Abstract: Continual post-training is becoming a central paradigm for adapting vision-language models to evolving tasks.
arXiv:2603. 11653v2 Announce Type: replace Abstract: Continual Reinforcement Learning (CRL) for Vision-Language-Action (VLA) models is a promising direction toward self-improving embodied agents that can adapt in openended, evolving environments.
The paper proposes a drift‑adaptive method for Domain‑Incremental Learning that tracks representation drift for each past domain‑class pair. By measuring how much a concept’s current representation deviates from a fixed anchor, the method assigns stronger preservation to high‑drift pairs and down‑weights highly drifted domains during prototype construction. Experiments on DomainNet, Office‑Home, CORe50, and CDDB‑Hard show that this approach maintains a strong accuracy‑forgetting balance while using less training memory and the smallest storage compared to other methods.
The paper introduces Trust Region Continual Learning, a hybrid approach that blends generative replay with a Fisher-metric trust region constraint to address catastrophic forgetting in sequential task learning. It demonstrates that, under local approximations, the update can be interpreted as a single-step Model-Agnostic Meta-Learning (MAML) process, where replay provides an old-task gradient and the Fisher penalty shapes curvature. Experiments on task-incremental diffusion image generation and continual diffusion-policy control show that this method achieves superior final performance, better retention, and faster early-task recovery compared to EWC, replay, and other continual meta-learning baselines.
arXiv:2603. 11201v3 Announce Type: replace-cross Abstract: The world is inherently dynamic, and continual learning aims to enable models to adapt to ever-evolving data streams.
arXiv:2606. 08452v1 Announce Type: new Abstract: In many real-world settings, data streams are nonstationary and arrive sequentially, requiring learning systems to adapt continuously without retraining from scratch.
arXiv:2605.12484v3 Announce Type: replace Abstract: Large language models (LLMs) are trained for downstream tasks by updating their parameters (e.g., via RL). However, updating model parameters force...
arXiv:2608. 15854v1 Announce Type: new Abstract: Catastrophic forgetting remains a fundamental obstacle to continual learning, where neural networks lose previously acquired knowledge while learning new tasks.
arXiv:2607. 05609v1 Announce Type: cross Abstract: The Continual Learning (CL) literature has long been driven by the goal of mitigating catastrophic forgetting.
The paper argues that catastrophic forgetting and loss of plasticity alone cannot explain why naive sequential training underperforms offline joint training. It introduces data co-observation as a third factor, showing that observing training data together consistently improves performance across supervised and self-supervised settings. The study also reinterprets common continual learning methods, suggesting that memory replay’s success stems from restoring co-observation benefits rather than merely mitigating forgetting.