Learning, Fast and Slow: LLMs That Adapt Continually
This article introduces Fast-Slow Training (FST), a new paradigm that interleaves prompt optimization (fast weights) with parameter updates (slow weights) to significantly improve LLM performance on math, code, and general reasoning tasks. FST outperforms weights-only training in data efficiency, performance ceiling, model plasticity, and continual learning.
Learning, Fast and Slow: LLMs That Adapt Continually
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