Distillation is a sophisticated technique in machine learning that enhances knowledge transfer between models. By observing not just the predicted tokens but also the distribution of potential tokens from a teacher model, the student model gains deeper insights. This process allows for a richer understanding of how tokens are predicted, ultimately improving the model's performance.
