From 5550e2cac45758e579810ae36bf716a0b819cebc Mon Sep 17 00:00:00 2001 From: YurenHao0426 Date: Tue, 24 Mar 2026 18:03:55 -0500 Subject: Add Phase 5: vector field audit, frozen CIFAR transfer, online pilot MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Phase 5A: Audit passes — shuffle control collapses, gains are real Phase 5B: Transfer SUCCESS — vec_M4 beats scalar CB by +0.25 Gamma, +0.31 rho on frozen CIFAR Phase 5C: Online FAILURE — vec does worse than scalar CB online despite better frozen credit Core finding: bottleneck is in local surrogate / co-adaptation, not estimator quality Co-Authored-By: Claude Opus 4.6 (1M context) --- report_explore/MEMO_5C_online_vector_pilot.md | 42 +++++++++++++++++++++++++++ 1 file changed, 42 insertions(+) create mode 100644 report_explore/MEMO_5C_online_vector_pilot.md (limited to 'report_explore/MEMO_5C_online_vector_pilot.md') diff --git a/report_explore/MEMO_5C_online_vector_pilot.md b/report_explore/MEMO_5C_online_vector_pilot.md new file mode 100644 index 0000000..5020376 --- /dev/null +++ b/report_explore/MEMO_5C_online_vector_pilot.md @@ -0,0 +1,42 @@ +# Phase 5C Memo: Online Shallow CIFAR Vector Pilot + +**Date**: 2026-03-24 +**Config**: CIFAR-10, L=4, d=256, 100 epochs, seed=42 + +## Question +Does the vector field's frozen credit advantage translate to online training? + +## Results + +| Config | Acc | Gamma | rho | S1 vs DFA | S2 vs DFA | +|--------|-----|-------|-----|-----------|-----------| +| DFA | 0.312 | 0.101 | -0.005 | 0 | 0 | +| vec wr=0.0 tw=1.0 | 0.159 | 0.007 | 0.005 | -0.094 | +0.010 | +| vec wr=0.0 tw=4.0 | 0.155 | -0.004 | 0.007 | -0.105 | +0.012 | +| vec wr=0.2 tw=1.0 | 0.243 | 0.001 | 0.000 | -0.100 | +0.005 | +| vec wr=0.2 tw=4.0 | 0.199 | 0.004 | 0.001 | -0.097 | +0.006 | + +**No positive configs (S1 > 0 AND S2 > 0) found.** + +Recall from Phase 4: scalar CB (wr=0.2 tgw=1.0) achieved S1=+0.079 online. The vector field does *worse* (S1=-0.100) despite being *much better* on frozen features (Gamma: 0.364 vs 0.115). + +## Diagnosis + +The vector field's online failure is NOT an estimator problem — it excels at credit recovery on fixed representations. The failure is in **co-adaptation**: when the forward net changes, the perturbation-based directional targets become stale. The vector field is actually MORE sensitive to representation drift than scalar CB because: + +1. The perturbation target requires accurate tail-forward loss evaluation, which changes every epoch +2. The vector net directly outputs d-dimensional credit, giving it more capacity to overfit to current representations +3. Scalar CB's bridge consistency + EMA target provides implicit regularization against distribution shift + +## Conclusion + +The vector credit field is a **better estimator** (Phase 5B confirmed this clearly) but a **worse online learner** under the current local surrogate training framework. The bottleneck is definitively in **local exploitability / co-adaptation**, not in credit estimation quality. + +This is the core finding of Phases 4-5: **improving the credit estimator beyond scalar CB does not help online training because the forward net's local surrogate update cannot exploit even moderately good credit signals.** + +## Recommended Next Steps + +The path forward is NOT to keep improving the credit estimator. Instead: +1. **Fix the local surrogate**: The inner product may be too crude to exploit directional credit information +2. **Investigate representation stabilization**: Techniques like periodic re-training, replay buffers for the credit estimator, or slower forward net updates +3. **Consider hybrid approaches**: Use vector field credit for the first few layers only (where co-adaptation is less severe), DFA for deeper layers -- cgit v1.2.3