# Introducing Nested Learning: A new ML paradigm for continual learning

DevFeed: [Introducing Nested Learning: A new ML paradigm for continual learning](<https://devfeed.tech/articles/introducing-nested-learning-a-new-ml-paradigm-for-continual-learning-6827.md>)

Original publisher: [Read original article](<https://research.google/blog/introducing-nested-learning-a-new-ml-paradigm-for-continual-learning/>)

Published: 2025-11-07T17:37:22Z

Content type: article

Language: en

Sources: [The latest research from Google](<https://devfeed.tech/sources/the-latest-research-from-google.md>)

Topics: [Machine Learning & Artificial Intelligence](<https://devfeed.tech/topics/machine-learning-artificial-intelligence.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [Network architectures](<https://devfeed.tech/topics/network-architectures.md>), [Neural Network](<https://devfeed.tech/topics/neural-network.md>), [LLMs](<https://devfeed.tech/topics/llms.md>), [Google](<https://devfeed.tech/topics/google.md>), [Algorithms & Theory](<https://devfeed.tech/topics/algorithms-theory.md>)

Tags: [algorithms-theory](<https://devfeed.tech/tags/algorithms-theory.md>), [architectures](<https://devfeed.tech/tags/architectures.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [google](<https://devfeed.tech/tags/google.md>), [large-language-models-llms](<https://devfeed.tech/tags/large-language-models-llms.md>), [machine-intelligence](<https://devfeed.tech/tags/machine-intelligence.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [neurips](<https://devfeed.tech/tags/neurips.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [research](<https://devfeed.tech/tags/research.md>)

## AI overview

Google Research introduces Nested Learning, a machine learning approach for continual learning that represents a model as interconnected, nested optimization problems. The approach aims to reduce catastrophic forgetting by jointly treating model architecture and training rules as multiple optimization levels with distinct information flows and update rates.

## Source excerpt

Algorithms & Theory