When Does Continual Learning Actually Need to Learn
SaxenaNayan · x · 2026-07-14
This preprint and accompanying article discuss a critical question: under what circumstances does continual learning actually require "learning".
The authors point out that existing methods for LLM continual learning—such as prompting, fine-tuning, reinforcement learning, and context compression—are often studied in isolation. This work attempts to place these methods within a unified problem framework, re-evaluating their boundaries of applicability and necessity in continual learning scenarios.
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