mlsanityData to 7 Oct 2026

A History of Meta-gradient: Gradient Methods for Meta-learning

arXiv 2202.09701 ↗First posted 20 Feb 2022cs.LGcited by 7

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Abstract (arXiv)

The history of meta-learning methods based on gradient descent is reviewed, focusing primarily on methods that adapt step-size (learning rate) meta-parameters.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 9 of its 57 references that are in the map. First posted on arXiv 20 Feb 2022.

Builds on

9 of 57 references.

References · 9

  1. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  2. 2016Learning to learn by gradient descent by gradient descentcited by 616
  3. 2018Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learningcited by 270
  4. 2017DeepStack: Expert-Level Artificial Intelligence in No-Limit Pokercited by 228
  5. 2018Meta-Gradient Reinforcement Learningcited by 156
  6. 2019Discovery of Useful Questions as Auxiliary Taskscited by 45
  7. 2018TIDBD: Adapting Temporal-difference Step-sizes Through Stochastic Meta-descentcited by 5
  8. 2018Metatrace Actor-Critic: Online Step-size Tuning by Meta-gradient Descent for Reinforcement Learning Controlcited by 5
  9. 2019Examining the Use of Temporal-Difference Incremental Delta-Bar-Delta for Real-World Predictive Knowledge Architecturescited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 0; 2024: 4; 2025: 0; 2026: 1.

7 papers.