bench: larger synthetic n_train + L=4 Stage-2 recast#113
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…re data for deep) At 16000 rows the synthetic datasets fell in the small-batch band ([8..256]), so an Optuna trial that drew batch=8 ran ~2000 steps/epoch (300k tiny GPU launches/trial at ~9% util) — the full synthetic sweep was ~days. Bumping to 32000 (>20k) puts them in the large-batch band ([512..4096]) => ~10 steps/epoch, ~100x faster, AND gives the deep arm more data for a fairer depth signal. Non-degeneracy R2<0.7 gate + relu≠elu + monotonicity all still hold at 32k. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…hold Recasts the Stage-2 results on the effective-monotone-layer axis L (plain: depth+1; residual/deep: 2*depth+2) rather than the plain/residual/deep flavor split, per the depth-4 monotone universal-approximation theorem (4 layers suffice). Reports best-at-L<=4 (sufficient) vs best-at-L>4 (excess) per dataset in its primary metric, plus an error-vs-L curve from the Optuna search trials (best CV objective at each sampled L, L=4 line marked). Reads committed results; no training. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This was referenced Jul 21, 2026
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What
Two Stage-2 synthetic-benchmark tweaks:
n_train16000 → 32000 (crosses the 20k large-batch band; more data for the deep arms)L = 4sufficiency thresholdDraft for tracking — this branch had no prior PR.
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