Combining parameter updates with context optimization lets LLMs learn new tasks 3x more efficiently while staying closer to their original capabilities and avoiding the forgetting that comes from pure fine-tuning.
This paper proposes Fast-Slow Training (FST), a method that combines two learning mechanisms for LLMs: updating model parameters (slow learning) and optimizing the input context (fast learning). By separating task-specific adaptation from general knowledge, FST achieves better sample efficiency, reduces catastrophic forgetting, and maintains the model's ability to learn new tasks over time.