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learn2learn: A Library for Meta-Learning Research

arXiv 2008.12284 ↗First posted 27 Aug 2020cs.LG cs.CV cs.ROcited by 69

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Abstract (arXiv)

Meta-learning researchers face two fundamental issues in their empirical work: prototyping and reproducibility. Researchers are prone to make mistakes when prototyping new algorithms and tasks because modern meta-learning methods rely on unconventional functionalities of machine learning frameworks. In turn, reproducing existing results becomes a tedious endeavour -- a situation exacerbated by the lack of standardized implementations and benchmarks. As a result, researchers spend inordinate amounts of time on implementing software rather than understanding and developing new ideas. This manuscript introduces learn2learn, a library for meta-learning research focused on solving those prototyping and reproducibility issues. learn2learn provides low-level routines common across a wide-range of meta-learning techniques (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 to foster a community around standardized software for meta-learning research.

Key facts. Cited by 69 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Aug 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 13; 2022: 23; 2023: 9; 2024: 9; 2025: 13; 2026: 2.

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  1. 2021A Near-Optimal Algorithm for Stochastic Bilevel Optimization via Double-Momentumcited by 106
  2. 2022Tutorial on amortized optimizationcited by 83
  3. 2021FLEX: Unifying Evaluation for Few-Shot NLPcited by 67
  4. 2021Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptationcited by 50
  5. 2021Generalization Bounds for Meta-Learning via PAC-Bayes and Uniform Stabilitycited by 29
  6. 2022Betty: An Automatic Differentiation Library for Multilevel Optimizationcited by 27
  7. 2022Few-shot Learning with Noisy Labelscited by 23
  8. 2022Hypernetworks for Zero-shot Transfer in Reinforcement Learningcited by 18
  9. 2022TorchOpt: An Efficient Library for Differentiable Optimizationcited by 17
  10. 2023Making Scalable Meta Learning Practicalcited by 17
  11. 2021On sensitivity of meta-learning to support datacited by 15
  12. 2022Boosting Natural Language Generation from Instructions with Meta-Learningcited by 15
38 more
  1. 2021Sign-MAML: Efficient Model-Agnostic Meta-Learning by SignSGDcited by 14
  2. 2022Online Bilevel Optimization: Regret Analysis of Online Alternating Gradient Methodscited by 14
  3. 2022Meta-Learning of Neural State-Space Models Using Data From Similar Systemscited by 11
  4. 2021X-METRA-ADA: Cross-lingual Meta-Transfer Learning Adaptation to Natural Language Understanding and Question Answeringcited by 10
  5. 2021Minimax and Neyman-Pearson Meta-Learning for Outlier Languagescited by 10
  6. 2022The Curse of Low Task Diversity: On the Failure of Transfer Learning to Outperform MAML and Their Empirical Equivalencecited by 10
  7. 2022Learning to Learn and Sample BRDFscited by 9
  8. 2022Meta-Learning with Self-Improving Momentum Targetcited by 9
  9. 2023Meta-learning in healthcare: A surveycited by 9
  10. 2022TIML: Task-Informed Meta-Learning for Agriculturecited by 7
  11. 2022Transductive Decoupled Variational Inference for Few-Shot Classificationcited by 7
  12. 2022Task-Adaptive Meta-Learning Framework for Advancing Spatial Generalizabilitycited by 7
  13. 2024Metamizer: a versatile neural optimizer for fast and accurate physics simulationscited by 7
  14. 2024Statistical Guarantees for Lifelong Reinforcement Learning using PAC-Bayes Theorycited by 7
  15. 2021A Channel Coding Benchmark for Meta-Learningcited by 6
  16. 2021Uniform Sampling over Episode Difficultycited by 6
  17. 2023Is Pre-training Truly Better Than Meta-Learning?cited by 6
  18. 2021MetaHDR: Model-Agnostic Meta-Learning for HDR Image Reconstructioncited by 4
  19. 2022Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networkscited by 4
  20. 2025Scalable Meta-Learning via Mixed-Mode Differentiationcited by 4
  21. 2025Differentiable Model Predictive Control on the GPUcited by 4
  22. 2021Embedding Adaptation is Still Needed for Few-Shot Learningcited by 2
  23. 2022Meta-Learned Kernel For Blind Super-Resolution Kernel Estimationcited by 2
  24. 2023Mitigating Catastrophic Forgetting for Few-Shot Spoken Word Classification Through Meta-Learningcited by 2
  25. 2023Writer adaptation for offline text recognition: An exploration of neural network-based methodscited by 2
  26. 2024FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocationcited by 2
  27. 2024Should Cross-Lingual AMR Parsing go Meta? An Empirical Assessment of Meta-Learning and Joint Learning AMR Parsingcited by 2
  28. 2024Episodic fine-tuning prototypical networks for optimization-based few-shot learning: Application to audio classificationcited by 2
  29. 2025qNBO: quasi-Newton Meets Bilevel Optimizationcited by 2
  30. 2025Multi-Objective Bilevel Learningcited by 2
  31. 2022Rethinking Task Sampling for Few-shot Vision-Language Transfer Learningcited by 1
  32. 2022A Transformer-Based Contrastive Learning Approach for Few-Shot Sign Language Recognitioncited by 1
  33. 2022Blinder: End-to-end Privacy Protection in Sensing Systems via Personalized Federated Learningcited by 1
  34. 2022Enhanced Meta Reinforcement Learning using Demonstrations in Sparse Reward Environmentscited by 1
  35. 2022On Stability and Generalization of Bilevel Optimization Problemcited by 1
  36. 2023Multilingual Sentence-Level Semantic Search using Meta-Distillation Learningcited by 1
  37. 2023Exploring Active Learning in Meta-Learning: Enhancing Context Set Labelingcited by 1
  38. 2023Advancing Image Retrieval with Few-Shot Learning and Relevance Feedbackcited by 1

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