Arctop has published a companion article to its paper introducing Reinforcement Learning from Brain Feedback (RLbF), a framework that decodes real-time EEG into cognitive states such as workload and stress and uses them as reward signals to train large language models. Unlike RLHF, which depends on sparse, subjective feedback given after the fact, RLbF supplies continuous, involuntary signals that let a model sense in real time how its words land in a listener's brain, which the article says improves communication. It is the first use of brain signals to train a language model and is already running in Arctop's Isaac app, though it adapts on a single dimension, cognitive workload, and technical details are not fully public.
