item 24 pre-registered: d=1 retry with loop-only band-LoRA r=16 (4.8M params, whole band, k=0 bit-exact); trainer/eval gain --bandlora; smoke-tested train+eval
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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@@ -630,3 +630,28 @@ chapter closes as scored in item 22; the loop program's live paths
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are state-side (rung-2 band-LoRA / wider merge, E2-N hardening,
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coarse-target auxiliary supervision matched to the medium) or the
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hybrid (A2), not longer/denser pause-chains.
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24. **E2-L d=1 with a trainable band: loop-only band-LoRA
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(pre-registered 2026-07-17 ~00:30, before running; Nils: "can we
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unfreeze the entire band and try this again?").** Item 23 located
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the constraint in the carried state's per-position transformation;
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this is the state-side attack. Full unfreeze rejected in design
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(642M params vs 427 examples, and it would break the frozen-model
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guarantee); instead LoopLoRA (lora_band.py, built for the rung-2
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design): rank-16 deltas on q/v/down of EVERY band layer (L14-30,
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uniform scale 1.0, 4.8M params), active ONLY during band re-runs —
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initial forward and k=0 stay bit-exact by construction. Otherwise
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identical to item-22 d=1: front-first deletion, 10 pauses/step,
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warm-start rung-A e400 (adapter lr 3e-4; fresh LoRA lr 1e-3, B
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zero-init so step-0 matches item 22 exactly), 200 steps, seed 0.
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Eval n=256: 0:0 (sanity, must equal base), 2:12, 2:16; e200.
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Decision rule (same bands as item 23, vs d=1's 31.6): >=+5
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(>36.6) = band expressivity was binding -> escalate (rank 64, all
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projections, or full-band rung ladder); within +-5 = the medium
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verdict stands even with a trainable band -> internalization line
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CLOSED, program pivots (A2 / E2-N / coarse-aux / divergence
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batch). Prediction, honest: mild gain but under threshold (~33-36)
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— the microscopy says the board's failure mode is WHAT it carries,
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not how flexibly it transforms it; but this is the experiment the
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'wider state' hypothesis deserves before the line closes. Job:
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scripts/jobs/zzz_p_rungb_blr.sh.
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