Learning, Fast and Slow: Towards LLMs That Adapt Continually [R]

Large language models (LLMs) face catastrophic forgetting and plasticity loss when updating parameters for downstream tasks. This work introduces a fast-slow learning framework for LLMs, utilizing model parameters as "slow" weights and optimized context as "fast" weights to adapt efficiently without compromising general reasoning.