Related work

The foundational work on continual learning, 1980 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

8 papers of 8,653Sort Recent · Most cited
  1. 2025
    What Can Grokking Teach Us About Learning Under Nonstationarity?Clare Lyle, Gharda Sokar, Razvan Pascanu, Andr'as GyorgyarXiv
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  2. 2025
    Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement LearningRafal Surdej, Michal Bortkiewicz, Alex Lewandowski … Clare LylearXiv
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  3. 2024
    Non-Stationary Learning of Neural Networks with Automatic Soft Parameter ResetAlexandre Galashov, Michalis K. Titsias, András György … Maneesh SahaniNeurIPS
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  4. 2024
    Normalization and effective learning rates in reinforcement learningClare Lyle, Zeyu Zheng, Khimya Khetarpal … Will DabneyNeurIPS
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  5. 2024
    Weight Clipping for Deep Continual and Reinforcement LearningMohamed Elsayed, Qingfeng Lan, Clare Lyle, A. Rupam MahmoodRLJ
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  6. 2024
    Disentangling the Causes of Plasticity Loss in Neural NetworksClare Lyle, Zeyu Zheng, Khimya Khetarpal … Will DabneyCoLLAs
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  7. 2023
    Deep Reinforcement Learning with Plasticity InjectionEvgenii Nikishin, Junhyuk Oh, Georg Ostrovski … André Sales BarretoNeurIPS
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  8. 2023
    Understanding plasticity in neural networksClare Lyle, Zeyu Zheng, Evgenii Nikishin … Will DabneyICML
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About this index

We keep this list because we read the field and wanted one place to see it. It covers work on continual learning itself, in the core areas of machine learning, and leaves out papers that apply it inside another field, such as medical imaging or fault diagnosis. By default it shows the papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or written by someone who has published there, or cited a few hundred times. The rest are one click away under “All papers”. It is seeded from the community lists kept by ContinualAI and by Xialei Liu, then filled out from OpenAlex, and every week a script looks for new papers on OpenAlex and arXiv. A model reads each candidate and decides whether it belongs; a person reviews the additions before they go live. Authors and affiliations come from OpenAlex, so a recent preprint can lack its institutions for a week or two.

Missing something, or filed under the wrong venue? Write to hello@unify.ai with the arXiv id or DOI.