Early Memory Selection for Balanced Adam

Abstract:We propose a method for choosing the shared memory parameter $\beta_1=\beta_2=\beta$ in Adam from a short pilot training. The selected $\beta$ remains fixed during the subsequent full training. A local model of Adam's normalized direction balances sampling variability against the delay introduced by averaging past gradients. This balance gives a cubic memory rule, whose two coefficients are estimated from gradient probes at a few pilot checkpoints. The estimator uses the numerator and denominator jointly, preserving their covariance. With a 200-update pilot and sixteen probe gradients at each of four checkpoints, a seed-matched retrospective evaluation on eleven vision and language workloads reduces mean relative validation gap by 40.7% and worst-quarter mean gap by 44.3% against the grid representative of shared $\beta=0.95$. The mean gap is also 32.3% lower than that of the best constant $\beta$ chosen across all eleven workloads.

Includes theoretical proofs and reproducibility appendices. Code and data: this https URL

Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)

arXiv:2610.08624 [cs.LG]

(or arXiv:2610.08624v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.08624 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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