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Key findings:
- deltaL (output-layer gradient) gives best Gamma (0.562 vs 0.452 for eT)
- Concatenating h_L to s destroys credit quality (value net cheats)
- Terminal gradient matching is monotonically beneficial
- Best config: deltaL + tgw=1.0 + wr=0.05 -> Gamma=0.768, rho=0.691
- CIFAR depth scan shows no Goldilocks regime (dimensionality bottleneck)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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scan
- synth_nonlinearity_ladder.py: teacher-student with phi_alpha(z) = (1-a)z + a*tanh(z)
Sweeps alpha x depth to find where state bridge / credit bridge fail
- cifar_depth_scan.py: CIFAR-10 with L={2,4,6,8,12}, d={256,512}
Finds Goldilocks regime for credit bridge vs DFA
- plot_synth_ladder.py: phase diagram visualization
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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All experiments complete:
- Toy LQ: credit bridge matches state bridge (~0.94 costate cosine)
- CIFAR-10: credit bridge (29.6%) comparable to DFA (30.0%), both beat state bridge (18.5%)
- State bridge confirms core hypothesis: perfect state prediction != useful credit
- Terminal gradient matching is essential for credit bridge
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Reason: toy used raw MSE, CIFAR used normalized. They must be the same method
for consistent reporting. Normalized MSE is more robust to varying h_L magnitudes.
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Debug phase. Toy LQ experiments (3 seeds) complete with terminal gradient matching.
Credit bridge matches state bridge on linear system (~0.94 cosine).
CIFAR experiments in progress.
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