7 stories tagged with #continual-learning, in publish-time order across the WeSearch catalog. Tag pages update as new stories ingest.
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HERO: A Heterogeneity-Aware Benchmark Library for Federated Continual Learning
Federated continual learning (FCL) evaluates how distributed clients learn from changing data streams while retaining previously learned knowledge. Existing evaluations are difficu…
Trajectory, founded by ex-DeepMind, Apple, and OpenAI staff to train "continual learning" models on user interactions, raised a $15M seed at a $115M valuation (Maxwell Zeff/Wired)
DRIVE: Modeling Skills at the Reasoning and Interaction Levels for Web Agents under Continual Learning
Web agents require both high-level reasoning (for task decomposition) and low-level interactions (for page elements manipulation) to conduct different tasks. However, these knowled…
CP-MoE: Consistency-Preserving Mixture-of-Experts for Continual Learning
Catastrophic forgetting remains a major obstacle to continual learning in large language models (LLMs) and vision--language models (VLMs). Although Mixture-of-Experts (MoE) archite…
Tunable MAGMAX: Preference-Aware Model Merging for Continual Learning
Continual learning (CL) aims to train models sequentially on multiple tasks while mitigating catastrophic forgetting of previously learned knowledge. Recent advances in large pre-t…
Shapley Neuron Values for Continual Learning: Which Neurons Matter Most?
Continual learning enables neural networks to learn tasks sequentially without forgetting previously acquired knowledge. However, neural networks suffer from catastrophic forgettin…
Self-Distillation Enables Continual Learning [PDF]
Continual learning, enabling models to acquire new skills and knowledge without degrading existing capabilities, remains a fundamental challenge for foundation models. While on-pol…