F. Rivest

Distilling Knowledge from Large Language Models into Lightweight Reinforcement Learning Agents for Autonomous Cyber Operations

The use of a Large Language Model (LLM) to improve autonomous defensive decision-making within an ACO environment is investigated and an online policy distillation framework is proposed that transfers the LLM's defensive policy into a lightweight RL agent containing only 64,910 parameters, reducing model size by several orders of magnitude while maintaining effective defensive capabilities.