ENG: The methodology proposed by researchers at MIT and Computer Science and Artificial Intelligence Laboratory is based on aligning internal representations between two neural networks during training. Instead of training a target network independently from random initialization, the approach introduces a guide network whose role is to shape the learning dynamics of the target. During an initial guidance phase, the target network is encouraged to produce intermediate activations that are similar to those of the guide network when both are exposed to the same inputs. This alignment is enforced through an auxiliary loss that measures representational similarity at multiple layers, effectively constraining the target network to explore more favorable regions of the parameter space.