The authors propose Divide-and-Remember (D&R), a recursive memory method for Vision-Language-Action (VLA) models that learns to remember relevant information from a long history of observations. This method is efficient, scalable, and achieves state-of-the-art results on a benchmark of long-horizon manipulation tasks.