Title: Adaptive Block-wise Learning for Knowledge Distillation. Abstract: Knowledge distillation allows the student network to improve its performance under the supervision of transferred knowledge. Existing knowledge distillation methods are implemented under the  implicit hypothesis that knowledge from teacher and student contributes to each layer of the student network to the same extent. In this work, we argue that there should be different contributions of knowledge from the teacher and the student during training for each layer. Experimental results evidence this argument. To the end, we propose a novel Adaptive Block-wise Learning~(ABL) for Knowledge Distillation to automatically balance teacher-guided knowledge between self-knowledge in each block. Specifically, to solve the problem that the error backpropagation algorithm cannot assign weights to each block of the student network independently, we leverage the local error signals to approximate the global error signals on student objectives. Moreover, we utilize a set of meta variables to control the contribution of the student knowledge and teacher knowledge to each block during the training process. Finally, the extensive experiments prove the effectiveness of our method. Meanwhile, ABL provides an insightful view that in the shallow blocks, the weight of teacher guidance is greater, while in the deep blocks, student knowledge has more influence.