The model-assisted safety pipeline is an alignment framework that uses the language models themselves as tools to steer model behavior toward safe and nuanced responses. Designed to address failure modes in standard RLHF—such as generating unsafe advice or becoming overly cautious by hedging and refusing benign queries—the framework consists of two primary components: an expanded set of safety-relevant training prompts and rule-based reward models (RBRMs) that supply targeted feedback during reinforcement learning fine-tuning.