Research

CLaaS: Continual Learning as a Service for Sample Efficient Online Learning

Kion Fallah, Silen Naihin, Barak Widawsky, Qingqing Mao

We investigate online continual learning for deployed LLM agents that must adapt to distribution shift. We propose CLaaS, a system which enables agents to improve during deployment, abstracted behind a chat API. To boost sample efficiency, CLaaS stores rollouts in an experience replay buffer for gradient reuse during asynchronous training. We evaluate on an adversarial task, finding that parametric updates lead to superior forward transfer and less forgetting than in-context learning.