Continual & Lifelong Learning Reading List

Curated by Mouhssine Rifaki | Stanford Electrical Engineering | Last updated August 2026

Continual learning aims to learn sequentially without forgetting. These papers introduce the core problems and the methods—regularization, replay, and architecture—that address them.

Continual & Lifelong Learning: 10 key papers

1. Overcoming catastrophic forgetting in neural networks
   Kirkpatrick et al. arXiv 2016.
2. Gradient Episodic Memory for Continual Learning
   Lopez-Paz and Ranzato. arXiv 2017.
3. Continual Learning Through Synaptic Intelligence
   Zenke et al. arXiv 2017.
4. Memory Aware Synapses: Learning what (not) to forget
   Aljundi et al. arXiv 2017.
5. Experience Replay for Continual Learning
   Rolnick et al. arXiv 2018.
6. Continual Lifelong Learning with Neural Networks: A Review
   Parisi et al. arXiv 2018.
7. Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence
   Chaudhry et al. arXiv 2018.
8. iCaRL: Incremental Classifier and Representation Learning
   Rebuffi et al. arXiv 2016.
9. Measuring Catastrophic Forgetting in Neural Networks
   Kemker et al. arXiv 2017.
10. Progressive Neural Networks
    Rusu et al. arXiv 2016.

Related reading lists

• Neural Architecture Search
• Memory-Augmented RL & Recurrent Policies
• Hindsight Experience Replay & Relabeling
• Policy Gradient Methods
• Neuroscience-Inspired Reinforcement Learning
• Multi-Agent Reinforcement Learning
• Long-Context & Memory Transformers
• Explainable AI & Interpretability