MoRe: Modular Representations for Principled Continual Representation Learning on Sequantial Data

arXiv:2605.14364v2 Announce Type: replace Abstract: 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 dis

Source: arXiv cs.LG — read the full report at the original publisher.

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