faeval.git/experiments/exploitability_samebatch_linesearch.py, branch master Unnamed repository; edit this file 'description' to name the repository. Add Phase 6.5A: same-batch linesearch REVISES Phase 6A conclusion 2026-03-25T13:22:04+00:00 YurenHao0426 Blackhao0426@gmail.com 2026-03-25T13:22:04+00:00 7e01fbc0ce871857c1e1879ed0d3559e8bfae7c7 Phase 6A's "better credit → worse loss" was a protocol artifact caused by: 1. Credit normalization (inflated DFA, suppressed Vec magnitude ordering) 2. Held-out evaluation (measured generalization failure, not exploitability) 3. Gradient clamping With strict same-batch evaluation: - Oracle BP: dL_same = -0.406 (strongest descent) - Vec_M4: dL_same = -0.135 - ScalarCB: dL_same = -0.025 - DFA: dL_same = -0.003 Same-batch loss decrease is MONOTONIC with credit quality. But held-out loss INCREASES for all non-DFA methods (Case D: overfitting). The bottleneck is batch-level generalization, not surrogate exploitability. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> 
Phase 6A's "better credit → worse loss" was a protocol artifact caused by:
1. Credit normalization (inflated DFA, suppressed Vec magnitude ordering)
2. Held-out evaluation (measured generalization failure, not exploitability)
3. Gradient clamping

With strict same-batch evaluation:
- Oracle BP: dL_same = -0.406 (strongest descent)
- Vec_M4: dL_same = -0.135
- ScalarCB: dL_same = -0.025
- DFA: dL_same = -0.003
Same-batch loss decrease is MONOTONIC with credit quality.

But held-out loss INCREASES for all non-DFA methods (Case D: overfitting).
The bottleneck is batch-level generalization, not surrogate exploitability.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>