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In this work, we present a novel methodology for continual learning called MERLIN: Meta-Consolidation for Continual Learning. We assume that weights of a neural network $\\boldsymbol \\psi$, for solving task $\\boldsymbol t$, come from a meta-distribution $p(\\boldsymbol{\\psi|t})$. This meta-distribution is learned and consolidated incrementally. We operate in the challenging online continual learning setting, where a data point is seen by the model only once. 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