Fast and Slow Variational Continual Learning

2026-06-22 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A new optimizer, Continual IVON (CoVON), is introduced to address challenges in continual learning for modern deep networks. It integrates fast and slow adaptation, inspired by neuroscience, into the Variational Continual Learning (VCL) framework. CoVON achieves slow adaptation by merging past posteriors to mitigate knowledge drift, which then serves as a prior for fast-weight updates. Implemented within the IVON optimizer, CoVON maintains a form and cost profile nearly identical to Adam. The optimizer consistently outperforms existing VCL optimizers and other weight-regularization strategies. Its effectiveness is demonstrated across various tasks, including domain-incremental learning, continual pre-training, and fine-tuning of large language models. This development was published on 2026-06-22.

Key takeaway

For Machine Learning Engineers designing models for dynamic data streams, CoVON offers a robust solution to the stability-plasticity dilemma. You should consider integrating this optimizer, which leverages merged past posteriors for slow adaptation and VCL for fast updates, to enhance model performance. This approach, demonstrated across continual pre-training and fine-tuning of large language models, can significantly improve knowledge retention and adaptation.

Key insights

Continual IVON (CoVON) integrates fast and slow adaptation via posterior merging within VCL, improving stability and plasticity in deep networks.

Principles

• Balance stability and plasticity.
• Use past posteriors as future priors.
• Merge posteriors for slow adaptation.

Method

Incorporate slow adaptation by merging past posteriors to reduce knowledge drift. Use the merged posterior as a prior in VCL updates for fast-weight adjustments, seamlessly implemented in IVON.

In practice

• Improve domain-incremental learning.
• Enhance continual pre-training.
• Fine-tune large language models.

Topics

• Continual Learning
• Variational Continual Learning
• CoVON Optimizer
• IVON Optimizer
• Deep Networks
• Large Language Models

Best for: Research Scientist, AI Engineer, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.