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Meta-learning researchers face two fundamental issues in their empiricalwork: prototyping and reproducibility. Researchers are prone to make mistakeswhen prototyping new algorithms and tasks because modern meta-learning methodsrely on unconventional functionalities of machine learning frameworks. In turn,reproducing existing results becomes a tedious endeavour -- a situationexacerbated by the lack of standardized implementations and benchmarks. As aresult, researchers spend inordinate amounts of time on implementing softwarerather than understanding and developing new ideas.  This manuscript introduces learn2learn, a library for meta-learning researchfocused on solving those prototyping and reproducibility issues. learn2learnprovides low-level routines common across a wide-range of meta-learningtechniques (e.g. meta-descent, meta-reinforcement learning, few-shot learning),and builds standardized interfaces to algorithms and benchmarks on top of them.In releasing learn2learn under a free and open source license, we hope tofoster a community around standardized software for meta-learning research.

learn2learn: A Library for Meta-Learning Research