From 29b92fb3146f57f4d0ff7cb76f073b387a0d4879 Mon Sep 17 00:00:00 2001 From: YurenHao0426 Date: Fri, 5 Jun 2026 14:52:14 -0500 Subject: Revise capacity theory to soft erosion --- notes/23_soft_capacity_theory_revision.md | 191 ++++++++++++++++++++++++++++++ 1 file changed, 191 insertions(+) create mode 100644 notes/23_soft_capacity_theory_revision.md (limited to 'notes/23_soft_capacity_theory_revision.md') diff --git a/notes/23_soft_capacity_theory_revision.md b/notes/23_soft_capacity_theory_revision.md new file mode 100644 index 0000000..bc5ff3c --- /dev/null +++ b/notes/23_soft_capacity_theory_revision.md @@ -0,0 +1,191 @@ +# Soft Capacity Erosion Revision + +The dense long-training sweep rules out the clean hard-exhaustion story as the +main theory. + +The previous hard-rank claim was: + +```text +Δd_hard = max(0, k - (P - d)) +``` + +This is still a useful sanity check for exact rank collapse, but it is too +coarse for FA/BP train-gap prediction. It predicts no functional loss before +redundant directions are exhausted. The dense sweep shows the gap changes +smoothly with capacity margin instead. + +## Correct Interpretation + +Capacity is eroded continuously. + +Alignment constraints do not wait until all redundant directions are exhausted. +They project into task-relevant directions with nonzero expected overlap from +the beginning. Overparameterization reduces the overlap, but does not make it +zero. + +So the right statement is not: + +```text +enough redundant capacity -> no FA/BP bias +``` + +The right statement is: + +```text +more redundant capacity -> smaller expected FA/BP bias +``` + +## Random-Subspace Soft Erosion + +Let: + +```text +P = parameter dimension +d = task-sensitive dimension +k = alignment constraint dimension +S = task-sensitive parameter subspace +E = alignment constraint subspace +P_S = projection onto S +P_E = projection onto E +``` + +The soft overlap is: + +```text +T_k = tr(P_E P_S) +``` + +For a random `k`-dimensional alignment subspace: + +```text +E[T_k] = k d / P +``` + +This is nonzero for every `k > 0`. Therefore, at the effective-capacity level, +there is no exact zero-loss region before hard exhaustion. There is only a +small-overlap region when `P` is large. + +The hard-rank loss: + +```text +max(0, k - (P - d)) +``` + +is a threshold for exact rank collapse, not a threshold for finite-time loss, +conditioning loss, or effective-dimension loss. + +## Tangent-Kernel Version + +Let `J` be the training-set output Jacobian. BP uses: + +```text +K_BP = J J^T +``` + +If alignment removes a random `k`-dimensional parameter subspace and leaves +projection `Q`, then the restricted tangent kernel is: + +```text +K_k = J Q J^T +``` + +For a random retained subspace of dimension `P-k`: + +```text +E[Q] = (1 - k/P) I +``` + +Therefore: + +```text +E[K_k | J] = (1 - k/P) K_BP +``` + +This is the cleanest correction. Before any hard rank collapse, the expected +tangent operator is already weakened by a factor proportional to `k/P`. + +For squared-loss gradient descent with residual `r_t`, each BP eigenmode with +eigenvalue `λ_i` contracts roughly as: + +```text +BP: (1 - η λ_i / N)^T +soft FA: (1 - η (1-k/P) λ_i / N)^T +``` + +So finite-time gap is positive for any `k > 0`, although it shrinks as `k/P` +shrinks. + +## Penalized Alignment Version + +If alignment is a soft penalty rather than a hard projection: + +```text +H_align = C^T C +K_λ = J (M + λ H_align)^(-1) J^T +``` + +For small `λ`: + +```text +K_λ = K_0 - λ J M^(-1) H_align M^(-1) J^T + higher-order terms +``` + +Again, there is no hard threshold. Any nonzero alignment pressure reduces +effective tangent capacity in task-relevant directions unless it is exactly +orthogonal to the task subspace. + +## Revised Contribution 2 + +Old wording: + +```text +FA/BP gap opens when redundant directions are exhausted. +``` + +New wording: + +```text +Feedback alignment induces a soft capacity erosion. The expected task-relevant +overlap of alignment constraints scales as k d / P, so redundancy suppresses but +does not eliminate the FA/BP gap. Hard rank exhaustion remains a limiting +boundary for exact rank collapse, while empirical train gaps follow a smooth +capacity-controlled ramp. +``` + +## What The Dense Experiment Supports + +Dense `T=30000` sweep: + +```text +margin -190 -> gap 0.1468 +margin -158 -> gap 0.1005 +margin -126 -> gap 0.0465 +margin -94 -> gap 0.0297 +margin -62 -> gap 0.0142 +margin -30 -> gap 0.0075 +margin 2 -> gap 0.0037 +margin 34 -> gap 0.0021 +margin 66 -> gap 0.0008 +margin 98 -> gap 0.0006 +margin 130 -> gap 0.0002 +``` + +This is exactly the qualitative shape expected from soft erosion: monotone, +smooth, and close to log-linear over the tested range. + +## Paper Consequence + +Contribution 2 should be renamed from: + +```text +Scaling Law and Redundancy Exhaustion +``` + +to something like: + +```text +Scaling Law and Soft Capacity Erosion +``` + +Hard exhaustion should appear as a corollary or limiting case, not the main +claim. -- cgit v1.2.3