Local Reinforcement Learning with Action-Conditioned Root Mean Squared Q-Functions

ArXi:2510.06649v2 Announce Type: replace-cross The Forward-Forward (FF) Algorithm is a recently proposed learning procedure for neural networks that employs two forward passes instead of the traditional forward and backward passes used in backpropagation. However, FF remains largely confined to supervised settings, leaving a gap at domains where learning signals can be yielded naturally such as RL. In this work, inspired by FF's goodness function using layer activity statistics, we