In deep reinforcement learning, multi-step learning is almost unavoidable to achieve state-of-the-art performance. However, the increased variance that multistep learning brings makes it difficult to increase the update horizon beyond relatively small numbers. In this paper, we report the counterintuitive finding that decreasing the batch size parameter improves the performance of many standard deep RL agents that use multi-step learning. It is well-known that gradient variance decreases with in…