One Learning Rate Doesn't Fit All: Layerwise Spectral Scheduling for Transformers

from Embodied AI 101 · host Shaoqing Tan

Shows that modern transformers are highly heterogeneous across layers and proposes layerwise learning rates based on weight spectrum shape, yielding up to 1.5× training speedup on LLaMA/GPT-style models.

Episode metadata supplied by the publisher feed · Published May 31, 2026

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One Learning Rate Doesn't Fit All: Layerwise Spectral Scheduling for Transformers

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