Title: Q-Ensemble for Offline RL: Don't Scale the Ensemble, Scale the Batch Size. Abstract: Training large neural networks is known to be time-consuming, where learning duration may stretch up to days or weeks. To address this problem, the approach of large-batch optimization was introduced, demonstrating that scaling mini-batch sizes with appropriate learning rate adjustments may speed up the training process by orders of magnitude. While long training time was not typically a major issue for model-free deep offline RL algorithms, recently introduced Q-ensemble methods achieving state-of-the-art performance made this issue more relevant, notably extending the training duration. In this work, we demonstrate how large-batch optimization, typically overlooked in deep offline RL community, can benefit this class of methods. We show that simply scaling the mini-batch size and naively adjusting the learning rate allows for (1) a reduced size of the Q-ensemble, (2) stronger penalization of out-of-distribution actions, and (3) improved convergence time, effectively shortening training durations by 2.5x times on average.