Cooldown shapes in WSD training trade off bias and variance, with sqrt and lowered-linear-0.7 shapes best for single runs, mirror-cosine best for averaging, and higher beta_2 during cooldown matching shape-selection gains.
The 210M parameter model is used in the majority of the experiments
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Training Dynamics of the Cooldown Stage in Warmup-Stable-Decay Learning Rate Scheduler
Cooldown shapes in WSD training trade off bias and variance, with sqrt and lowered-linear-0.7 shapes best for single runs, mirror-cosine best for averaging, and higher beta_2 during cooldown matching shape-selection gains.