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Machine Learning

Learning, Fast and Slow: Towards LLMs That Adapt Continually

Rishabh Tiwari, Kusha Sareen, et al.

Featured May 26, 2026

Original Paper

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Simply

A new training method helps big AI models learn new skills super fast using smart prompts while also slowly improving their core knowledge, preventing them from forgetting old lessons and staying flexible for future tasks.

In depth
The paper introduces Fast-Slow Training (FST), a novel framework that enables large language models (LLMs) to adapt continually by combining two complementary learning mechanisms: slow parametric updates to the model's weights () and fast textual context optimization of prompts (). This dual-channel approach allows LLMs to rapidly acquire new task-specific knowledge through flexible prompts while preserving their general reasoning abilities and plasticity by minimizing changes to the core model parameters, thereby mitigating catastrophic forgetting.

Key Takeaways

  • 1
    The proposed Fast-Slow Training (FST) framework significantly improves data efficiency, allowing LLMs to reach higher performance asymptotes with fewer training samples.
  • 2
    FST effectively preserves plasticity and reduces catastrophic forgetting by distributing adaptation across model parameters and textual prompts, keeping the slow weights closer to the base model.
  • 3
    The method demonstrates superior continual learning capabilities, enabling LLMs to adapt to new tasks on the fly without degrading performance on previously learned skills.

Conceptual Flow

HIGH LEVEL
1
Methodology (The 'Logic')

Instead of just changing the AI's brain, this method also gives it smart notes (prompts) that change quickly, helping it learn new things without forgetting old ones.

AI Model's Brain
Smart Notes
Co-evolve
Better AI Learning
2
Results (The 'Impact')

The new method learns much faster, remembers more, and stays better at learning new things compared to just changing the AI's brain.

Old Way
New Way
Compare Learning
Faster Learning
Less Forgetting
More Flexible