ReBRAC-v2: The Return of the King

ReBRAC-v2 revisits a conventional behavior-regularized actor-critic instead of relying on increasingly specialized generative policies and value guidance. It uses an exact-likelihood normalizing-flow actor, mixed likelihood/MSE/MAE behavior regularization, a classification-based residual critic, staged optimization, and multi-sample action selection. A shared recipe tuned through roughly 600 Bayesian proposals on six OGBench tasks is then evaluated across ten state-based categories, reaching a 74.8 average versus 52.3 for the next-best aggregate result and ranking first in eight categories. The same recipe reports averages of 90.2 on D4RL AntMaze and 33.6 on Adroit.