Learning, Fast and Slow: Towards LLMs That Adapt Continually [R]

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A new framework called Fast-Slow Training (FST) has been introduced for Large Language Models (LLMs), aiming to bridge the gap between in-context learning and parameter updates. This approach allows LLMs to rapidly adapt by using optimized context as ‘fast’ weights, while keeping their underlying parameters (‘slow’ weights) closer to a base model.

Research demonstrates that FST can achieve up to three times more sample efficiency compared to traditional reinforcement learning (RL), which is crucial for maintaining performance and preventing catastrophic forgetting. Moreover, models trained with FST retain significantly less drift from the original LLM baseline, reducing the likelihood of losing their foundational knowledge.

• FST maintains a balance between rapid adaptation through context optimization and preserving robustness via parameter stability.
• This approach is particularly beneficial in continual learning scenarios where tasks frequently change, as FST models can more effectively acquire new tasks without stalling like RL-trained models.
• The reduction in catastrophic forgetting achieved with FST ensures that LLMs remain adaptable and capable of handling multiple tasks over time, a crucial aspect for real-world applications such as conversational agents or knowledge management systems.

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This framework represents an important step towards creating more versatile and resilient AI models.

Originally published at reddit.com. Curated by AI Maestro.

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