← 研踪 PaperTrailarXiv:2611.10730 · v1
Breaking Precision Walls in First-, Second-, and Zeroth-Order Optimizers: One Constant, Set in Advance
首次提交 2026-11-07 21:43
版本更新 2026-11-07 21:43
雷达观测 2026-11-10 11:14
摘要
Under power-law signal decay across directions, an optimizer can fit strong directions and miss weak ones while average loss hides it. A precision wall measures the steepest decay where the weakest direction recovers within 5% in a fixed budget. On low-rank matrix sensing and quadratics one constant sets the wall: once it dominates the denominator, Adam or grafted Shampoo is momentum descent at step $η/c$, setting the wall; in Shampoo, matching grafting's constant to damping restores the wall after three hypotheses failed. In zeroth-order Adam, $\varepsilon$ acts against $\varepsilon^\ast$ from a mean-square spectral model; below $\varepsilon^\ast$ error hits a noise floor. Set from true curvature or spectrum on new shapes, the rule raised the wall above the same-step default by a forecast amount: on two sensing shapes by $1.96, 1.15$ (Adam) and $1.59, 1.74$ (grafted Shampoo), and on two Hadamard-rotated quadratics by $1.12, 0.73$ (zeroth-order Adam, longer budget), rising on all seeds; fixed-step momentum at that step reaches those walls. A cosine-scheduled default (small constant giving weak modes large steps) is higher on one sensing shape, matched on new seeds of the other, and lower with the rule's constant. A default step retuned per budget catches up on rotated quadratics and population logistic regression, fails at 1 query/step on two diagonal quadratics (catching up at 16), and exceeds the rule on digits logistic regression (full-batch and minibatches, where gradient noise sets the $η= 0.01, 0.1$ default walls). On logistic regression the rule did not raise the first-order wall: same-step default Adam was higher (its wall on 4 new shapes at the forecast stability limit), while in zeroth order on the population loss setting $\varepsilon$ raised the wall by $0.98, 0.88$.
作者备注：23 pages, 6 figures, 9 tables
观测到的版本
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v1 · 2026-11-07 21:43 · 最新观测版本
Breaking Precision Walls in First-, Second-, and Zeroth-Order Optimizers: One Constant, Set in Advance
Under power-law signal decay across directions, an optimizer can fit strong directions and miss weak ones while average loss hides it. A precision wall measures the steepest decay where the weakest direction recovers within 5% in a fixed budget. On low-rank matrix sensing and quadratics one constant sets the wall: once it dominates the denominator, Adam or grafted Shampoo is momentum descent at step $η/c$, setting the wall; in Shampoo, matching grafting's constant to damping restores the wall after three hypotheses failed. In zeroth-order Adam, $\varepsilon$ acts against $\varepsilon^\ast$ from a mean-square spectral model; below $\varepsilon^\ast$ error hits a noise floor. Set from true curvature or spectrum on new shapes, the rule raised the wall above the same-step default by a forecast amount: on two sensing shapes by $1.96, 1.15$ (Adam) and $1.59, 1.74$ (grafted Shampoo), and on two Hadamard-rotated quadratics by $1.12, 0.73$ (zeroth-order Adam, longer budget), rising on all seeds; fixed-step momentum at that step reaches those walls. A cosine-scheduled default (small constant giving weak modes large steps) is higher on one sensing shape, matched on new seeds of the other, and lower with the rule's constant. A default step retuned per budget catches up on rotated quadratics and population logistic regression, fails at 1 query/step on two diagonal quadratics (catching up at 16), and exceeds the rule on digits logistic regression (full-batch and minibatches, where gradient noise sets the $η= 0.01, 0.1$ default walls). On logistic regression the rule did not raise the first-order wall: same-step default Adam was higher (its wall on 4 new shapes at the forecast stability limit), while in zeroth order on the population loss setting $\varepsilon$ raised the wall by $0.98, 0.88$.
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