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papersTODAY 04:00 UTC

arXiv Paper Analyzes Optimal Learning Rate Schedules Under Functional Scaling Laws

A new arXiv preprint examines how learning rate schedules can be optimized within the functional scaling law framework, which separates training dynamics into signal learning and noise forgetting. The authors analyze power-law kernel regression to characterize these two components, comparing schedules such as power decay and warmup-stable-decay. The work offers theoretical guidance on choosing learning rate schedules for model training.

functional-scaling-laws kernel-regression learning-rate-schedules power-decay scaling-laws warmup-stable-decay

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arXiv cs.LGOptimal Learning Rate Schedules under Functional Scaling Laws: Power Decay and Warmup-Stable-Decay ↗TODAY 04:00 UTC