  Knowledge Transfer has been applied in solving a wide variety of problems.
For example, knowledge can be transferred between tasks (e.g., learning to
handle novel situations by leveraging prior knowledge) or between agents (e.g.,
learning from others without direct experience). Without loss of generality, we
relate knowledge transfer to KL-divergence minimization, i.e., matching the
(belief) distributions of learners and teachers. The equivalence gives us a new
perspective in understanding variants of the KL-divergence by looking at how
learners structure their interaction with teachers in order to acquire
knowledge. In this paper, we provide an in-depth analysis of KL-divergence
minimization in Forward and Backward orders, which shows that learners are
reinforced via on-policy learning in Backward. In contrast, learners are
supervised in Forward. Moreover, our analysis is gradient-based, so it can be
generalized to arbitrary tasks and help to decide which order to minimize given
the property of the task. By replacing Forward with Backward in Knowledge
Distillation, we observed +0.7-1.1 BLEU gains on the WMT'17 De-En and IWSLT'15
Th-En machine translation tasks.
