AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work studies optimal learning rate (LR) schedules under the functional scaling law (FSL) framework, which decomposes training dynamics into signal learning and noise forgetting, and shows that precise tuning of the decay shape is often unnecessary.
This work presents a systematic study of scale vectors in LLMs from the perspectives of expressivity, optimization, and architectural structure, and proposes three lightweight and complementary improvements to scale vectors: branch-specific heterogeneity, improved placement around linear mappings, and magnitude-directi...
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