Experience replay is a simple and well-performing strategy for continual learning problems, often used as a basis for more advanced methods. However, the dynamics of experience replay are not yet well understood. To showcase this, we focus on a single component of this problem, namely choosing the batch size of the buffer samples. We find that small batches perform much better at stopping forgetting than larger batches, contrary to the intuitive assumption that it is better to recall more samples from the past to avoid forgetting. We show that this phenomenon does not disappear under learning rate tuning and we propose possible directions for further analysis.