The Sequence Opinion #394: Models that Learn All the Time? Some Cutting Edge Ideas about Continual Learning
Was this email forwarded to you? Sign up here The Sequence Opinion #394: Models that Learn All the Time? Some Cutting Edge Ideas about Continual LearningModularity, sparcity, MoEs and other ideas that can unlock continual learning.
Continual learning is a key aspiration in the development of foundation models. Current pretraining-based methods typically require building models from scratch using large datasets and extensive computational resources. Despite its importance, progress in continual learning has been slow. However, recent advancements offer promising solutions, especially through modular architectures like Mixture of Experts (MoEs). This essay explores how continual learning enhances Large Language Models (LLMs), discusses current limitations, and highlights modularity’s role in overcoming these challenges. Limitations of Current Pretraining ApproachesLLMs have revolutionized numerous fields, but traditional pretraining methods present significant limitations for continual learning:... Subscribe to TheSequence to unlock the rest.Become a paying subscriber of TheSequence to get access to this post and other subscriber-only content. A subscription gets you:
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