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Intermediate layer knowledge distillation (KD) can improve the standard KDtechnique (which only targets the output of teacher and student models)especially over large pre-trained language models. However, intermediate layerdistillation suffers from excessive computational burdens and engineeringefforts required for setting up a proper layer mapping. To address theseproblems, we propose a RAndom Intermediate Layer Knowledge Distillation(RAIL-KD) approach in which, intermediate layers from the teacher model areselected randomly to be distilled into the intermediate layers of the studentmodel. This randomized selection enforce that: all teacher layers are takeninto account in the training process, while reducing the computational cost ofintermediate layer distillation. Also, we show that it act as a regularizer forimproving the generalizability of the student model. We perform extensiveexperiments on GLUE tasks as well as on out-of-domain test sets. We show thatour proposed RAIL-KD approach outperforms other state-of-the-art intermediatelayer KD methods considerably in both performance and training-time.

RAIL-KD: RAndom Intermediate Layer Mapping for Knowledge Distillation