Title: Enforcing zero-Hessian in meta-learning. Abstract: Gradient-Based Meta Learning (GBML) enables us to get task-specific parameters with few-labeled datapoints in an inner loop. However, it has not yet been discussed how GBML can adapt to a new task within a few optimization steps with a huge learning rate in the inner loop. We find that the gradient does not change from the beginning to the end of the inner loop, meaning that it behaves like a linear model. In this paper, we argue that this characteristic is an essential key to understanding convergence in inner loops with huge learning rates. Also, we show that gradient-based meta learning can be interpreted as metric-based meta learning when we adopt our hypothesis that linearity in the inner loop is the key to operating GBML. To empirically prove and exploit our hypothesis, we propose a regularization-based algorithm called enforcing Linearity in the Inner Loop (LIL) which exploits our observation which can be applied to any baselines that has the form of GBML. LIL proves its potential by showing its boosted performance not only on top of general baselines in various architectures, but also on adverse or Hessian-free baselines. Qualitative experiments are also conducted to explain the performance of LIL.