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diff --git a/report_explore/MEMO_5B_frozen_vector_transfer.md b/report_explore/MEMO_5B_frozen_vector_transfer.md new file mode 100644 index 0000000..32b6325 --- /dev/null +++ b/report_explore/MEMO_5B_frozen_vector_transfer.md @@ -0,0 +1,33 @@ +# Phase 5B Memo: Frozen CIFAR Vector Credit Transfer + +**Date**: 2026-03-24 +**Config**: CIFAR-10, frozen BP reference (L=4, d=256, 61.7% acc), 100 epochs estimator training + +## Question +Can the direct vector credit field recover better credit than scalar CB on frozen real-task representations? + +## Results + +| Method | mean Gamma | mean rho | mean nudge | +|--------|-----------|---------|-----------| +| DFA | 0.005 | 0.005 | -0.000006 | +| ScalarCB_eT | 0.115 | 0.120 | -0.000370 | +| StateBridge_eT | 0.287 | 0.264 | -0.000957 | +| **Vec_eT_M4** | **0.364** | **0.426** | **-0.001406** | +| Vec_eT_M8 | 0.364 | 0.396 | -0.001379 | +| Vec_eT_M16 | 0.368 | 0.422 | -0.001393 | + +## Key Findings + +1. **Transfer SUCCESS**: Vector field outperforms scalar CB by +0.25 Gamma and +0.31 rho (both >> 0.05 threshold). + +2. **Vector field surpasses state bridge on rho** (0.43 vs 0.26), the most important no-BP-needed metric. On Gamma, vector field (0.36) is slightly above state bridge (0.29). + +3. **M=4 is sufficient** on d=256 frozen CIFAR. No improvement from M=8 or M=16. The perturbation target provides enough signal even with 4 directions in 256 dimensions. + +4. **Layer gradient**: Vector field credit quality increases with depth (layer 3: Gamma=0.61, rho=0.68). This is consistent with the terminal matching loss being strongest at the last layer. + +## Verdict + +**TRANSFER SUCCESS.** Proceed to Phase 5C (online shallow CIFAR). +Best config for Phase 5C: vec_eT_M4 (cheapest, equally good). |