Pre-registration: paired 40k arms — control vs aux-supervision-off

Execution moved (17:2xZ): the arms run in parallel on the owner-granted 4×H100 box, with two added control seed replicates — see the box batch pre-reg. Arms, flags, seed, and the primary read are unchanged; this post remains the governing science. The local sequential launcher is fallback only.

2026-08-05. Immutable once posted; the launcher header points here. Supersedes the own-baseline pre-registration per the owner’s 16:13Z steering (“make something else ready” + GPU never idles): the standalone 100k baseline is replaced by a paired 40k design where arm A doubles as the own-topology control. The originally slated treatment (unwrap-at-load) was killed by the wrap census — wraps touch 0.19% of training episodes, far inside pairing noise — so arm B goes to the next-best zero-new-code treatment: aux attribution (idea #6, a still-owed mainline question).

Question

Does aux text supervision (the CE loss over judge-annotation fields, weight 0.5) change action performance at matched steps, seed, and data — or is narration a free rider on the action objective? Mainline pre-registered “within probe noise (±0.3)” but never ran the paired arms; the 100k run answered only “does conditioning help at inference”. Secondary payoff: arm A is the charter §4 own-baseline (topology control) at 40k for every future training delta on this box.

Arms (sequential on the 1×H100, chained in one launcher)

• Arm A (control): fontaine_arb_rcond_40k_1xh100 — the mainline-best recipe verbatim from the superseded pre-reg (B10, workers 16, prefetch 4, LRs 1e-4/2e-5 unrescaled, seed 0), --steps 40000, --warmup-steps 1000, save-every 5000, eval-every 500.
• Arm B (treatment): fontaine_arb_rcond_auxoff_40k_1xh100 — identical except the aux loss is OFF: --aux-fields, --aux-dropout, --field-dropout omitted (“train actions only, the historical objective” path). Conditioning flags (--condition-fields subgoal outcome smoothness, --condition-dropout 0.1, --subgoal-dropout 0.5, --instruction-augment 0.5, --camera-kind-dropout 0.1) stay — the arms isolate the aux CE term alone.
• After both arms: frozen-panel evals of A@40k and B@40k with --dump-predictions, so the paired per-frame analysis runs on CPU without re-scoring.

40k (not 100k) per the owner-amended cap: early ablations pair at ≤ 40k; extend later only if a decision needs it.

Numbered expectations

1. Startup (both arms): selection 878 datasets / 42,872 episodes; ~11M decoder params + text trunk at 2e-5. Arm B’s model line shows no aux fields and its log lines carry no loss_aux.
2. Throughput: 0.4–0.6 s/step at B10 (smoke measured 0.39–0.45); VRAM < 76 GiB (smoke peak 67.4); ~5–6.5 h per arm, both arms + evals inside ~14 h.
3. Curves (256-frame in-run probe, ±0.3 floor): arm A below 12 by 10k and below 9 by 30k (inherited from the superseded pre-reg). Arm B’s action probe within ±0.3 of arm A at matched steps — the mainline pre-registered expectation, now actually tested. Arm B total loss is lower by construction (one term fewer); only the action component is comparable.
4. Primary read: panel chunk_mae A@40k vs B@40k, paired per-frame (same 17,204 core frames). Decision rule: |Δ| within pairing noise (bootstrap CI from the paired per-frame deltas) → aux supervision is action-neutral at this scale — banked, idea #6 closed at 40k/eff-10. B better than A beyond noise → the aux term taxes actions (weight 0.5 too high — flag to mainline, follow-up arm at lower weight). A better than B beyond noise → aux supervision helps actions (representation shaping is real — strengthens the aux program).
5. Kill gates (per arm): probe > 15 at 10k with a falling-then-rising shape; NaN loss; second OOM after the standing B−1 resume. Slowness alone is data. Arm A being killed does not launch arm B (the pair is the experiment; B alone answers nothing).

Known seams and confounds

• Same seed (0) in both arms: data order, augmentations, and dropout masks are as matched as the harness allows; residual nondeterminism (cuDNN autotune, atomics) is the pairing-noise floor the bootstrap CI measures.
• Arm B changes the sequence content (no aux value lines rendered before BOA), so its per-step token count differs — throughput and loss scale shift is expected and not a finding.
• eff-10 vs mainline eff-40: any cross-topology comparison stays directional; the paired A-vs-B contrast is the claim-grade result.
• 40k is 40% of the recipe’s step budget; a null here bounds the effect at this horizon only.

Cost

~11–13 h GPU for both arms + ~2×35 min panel evals (25,800 frames at the measured ~320 f/min), ~384 GB checkpoints (8 saves/arm × 24 GB; 2.2 T free), zero API spend. Launches tonight the moment the sealed-panel baseline score frees the GPU, gated on the smoke’s E1–E4 (passed 16:32Z except final-step formality — see log).