When Does Continual Learning Actually Require Learning?
YutongBAI1002 · x · 2026-07-14
This article discusses when continual learning in large language models truly requires "learning."
The author points out that existing continual learning methods are often studied in isolation, such as prompting, fine-tuning, reinforcement learning, and context compression. The team's work attempts to place these approaches within a unified framework, re-examining their roles in continual learning scenarios.
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