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.

9 papers of 8,653Sort Recent · Most cited
  1. 2025PDF ↗
  2. 2024
    Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay BuffersGautham Vasan, Mohamed Elsayed, Alireza Azimi … A. Rupam MahmoodNeurIPS
    PDF ↗
  3. 2024
    Weight Clipping for Deep Continual and Reinforcement LearningMohamed Elsayed, Qingfeng Lan, Clare Lyle, A. Rupam MahmoodRLJ
    PDF ↗
  4. 2024PDF ↗
  5. 2023PDF ↗
  6. 2023
    Maintaining Plasticity in Deep Continual LearningShibhansh Dohare, J. Fernando Hernandez-Garcia, Parash Rahman … Richard S. SuttonarXiv
    PDF ↗
  7. 2023PDF ↗
  8. 2022
    Memory-efficient Reinforcement Learning with Value-based Knowledge ConsolidationQingfeng Lan, Yangchen Pan, Jun Luo, A. Rupam MahmoodTrans. Mach. Learn. Res.
    PDF ↗
  9. 2021
    Continual Backprop: Stochastic Gradient Descent with Persistent RandomnessShibhansh Dohare, Richard S. Sutton, A. Rupam MahmoodarXiv · University of Alberta
    PDF ↗
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. 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.