Thus gradients of parameters indicate how should the parameters be updated.
furthermore, using loss gradient as one kind of meta information is inspired by MetaNet (Munkhdalai and Yu, 2017) and MAML (finn et al., 2017) which explore methods for few-shot learning by metalearning.