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9 hoursDepth-utility ladder: trainable-block sweep (BP/FA/DFA) on ResMLP CIFAR-10HEADmasterYurenHao0426
Appendix experiment triangulating the depth-utility diagnostic (D3) by varying the number of trainable residual blocks k (last-k trainable, first L-k frozen at init; embed/LN/head always trained). - d=256 L=4 and d=512 L=2, 3 seeds, recipe identical to the main audit. - BP climbs monotonically (+22-23pp); DFA peaks at the frozen baseline (k=0) and declines once any deep block is trained; FA shows partial/no net depth utility. - Cross-checks reproduce existing anchors (BP 0.617, DFA 0.301, FA 0.402, frozen 0.349). - frozen_init_identity_check quantifies frozen stack as a near-norm-preserving random feature map (per-block ||f||/||h||~0.10, stack cos 0.981), explaining the above-chance k=0 rung. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>