Pre-reg (FINAL) — grasp-rich SFT bootstrap: competence before RL pressure

2026-08-14, ~22:2xZ. Drafted at the owner’s go (22:07Z: “do as much in parallel as you reasonably can”) following the 21:53Z question — “what should we do next to train a policy which solves over 90% of seeds successfully?” Status: FINAL as of 2026-08-15 ~01:4xZ — §6 freezes the checklist (demo seeds, targets, recipe params, arm list, convention seam) and opens the objection window. Stage A landed CPU-side while the draft stood (14/16 on the engineering smoke).

Plain words. Our robot policies touch the toy boat but almost never finish the job — the best row completes the task on about 1 of 100 tries. Reinforcement learning can only amplify behaviors a policy already produces sometimes; at a 1% success rate there is nearly nothing to amplify, which is exactly what our last RL run measured (it learned to shove the boat, not grasp it). The unlock is to first teach the policy to grasp, using something we have for free in simulation: the simulator knows exactly where every object is, so a scripted robot — no learning, just move-above/close/lift/place against known coordinates — can generate hundreds of successful demonstrations cheaply. We then fine-tune the policy on those successes and only afterwards apply RL to push the success rate up. This document freezes that plan into measurable stages with abort rules, so each step tells us something even when it fails.

§1 Hypothesis and the causal chain

H: the policy’s ~0–1% sim success floor is a competence gap (no grasp behavior in-distribution), not an incentive gap — so dense successful demonstrations move success rate where GRPO re-pricing measurably did not (R1-B frozen verdict: deciding behavior worsened at pinch competence ~4/64; the registered next shape is grasp-rich SFT before RL pressure).

Chain to the owner’s 90% target: scripted expert (privileged state) → successful demos → SFT (≥20% base) → fresh-pre-reg GRPO per Decision 11 (groups finally carry success variance) → success-recycle flywheel for the tail. This pre-reg covers the chain UP TO the ≥20% gate; GRPO is its own registration.

§2 Stages, gates, and pricing (measured pace 0.0094 GPU-h/episode)

Worst-case ≈ 11 GPU-h; proposed gate ≤ 13 (both iteration arms included). Stage boundaries are hard stops with in-channel posts.

§3 Contamination + comparability guards (frozen at draft)

• Eval seeds 0–99 NEVER appear in demos: demo seeds drawn from 1000+ (same spawn distribution class, disjoint stream). The frozen sim100 seed set stays a pure holdout for every read in the chain.
• Demos are sim-rendered under the production visual config (v3 + fitted lens + numpy post) — the policy trains on the pixels it will be evaluated on; any visual-config change between B and D is a registered amendment.
• The honesty instrument (er_60k knn5) never trains — unchanged.
• Success detection = the sim100 harness’s own success_tick machinery (no new success definition).
• Seed policy: fresh demo seeds are variance-motivated (the standing policy’s stated-reason branch); the eval seeds stay the frozen 0–99 for comparability with every banked row.

§4 Falsifiers and consequences

• F-physics — the scripted expert with privileged state can’t reach ≥ 70%: our sim (contact/servo model) can’t host the grasp; demos would teach nothing. Escalate to the Squint twin tier (preflight GO banked 08-14) where competence is buildable from state, and the sim keeps its screening role only.
• F-transfer — script succeeds, SFT’d policy stays < 5/100: the gap is observation-side (visual/pose OOD), not competence — the wrist-transfer screen’s read (running in parallel) becomes the binding diagnosis, and renderer-class/visual work outranks more demos.
• F-live — ≥ 20/100: competence-first confirmed; GRPO registers fresh on the new stack (Decision 11) with success-variance groups, targeting the 90% north star via SFT+GRPO alternation.

§5 Interplay with the wrist-transfer screen (running in parallel)

The screen prices whether wrist-appearance honesty moves behavior; this bootstrap prices whether competence moves success. They share the sim100 substrate but not GPU windows (screen first — it is already registered FINAL). If the screen lands F-null/F-flat (wrist channel dead/tolerated), stage-C’s optional flow arm drops and the AR arm carries alone; if F-live, the demo render config inherits whatever wrist-fidelity decision the owner takes, as a registered amendment before stage B.

§6 Status — FINAL (frozen 2026-08-15 ~01:4xZ)

The draft checklist, resolved item by item:

(1) Frozen parameters.

• Stage-A gate read: 20 held seeds 1020–1039, ≥ 70% (≥ 14/20) scripted success, rendered (videos banked for the record, ~0.2 GPU-h class). Honesty note: seeds 1000–1015 were the engineering smoke set the expert’s fixes were tuned on (14/16 there) — the gate runs on a disjoint stream precisely so tuning-to-the-smoke can’t pass it.
• Stage-B collection: demo seeds ascend from 1000 (the smoke seeds are legitimate demos), production visual config, successes kept. Target 400 kept successes; gate ≥ 300 within ≤ 4 GPU-h.
• Stage-C primary (AR): molmoact2 train_lerobot.py, base allenai/MolmoAct2-SO100_101, the rig-ft recipe class verbatim — ft_action_expert=true only (ft_vlm=false, ft_embedding=none, lora=false), action-expert LR 5e-5, global batch 64 (device 8), save every 500 — with max_duration 3000 steps (the runbook’s 2000 was sized for the smaller rig set; 300–600 kept demos ≈ 45–90k frames ≈ 2–4 epochs at batch 64; every 500-step checkpoint is retained as fallback). Endpoint = final step.
• Stage-C optional flow arm: ftrig4k recipe verbatim (4k steps, decoder LR 1e-5, dataset swapped to the demo set). The wrist screen closed F-instrument (01:3xZ 08-15) — not F-null/F-flat — so the §5 drop clause does not fire: the flow arm stays in, conditional only on the ≤ 13 GPU-h gate after the primary chain lands.
• Stage-D arm list: the SFT-AR endpoint on the frozen sim100 (100 eval seeds, standard gates + reset-strike checks); the flow endpoint too if trained. Context anchors (not gates): banked ftrig4k +0.08 cm / 47 moved / ~1 success class, and the fresh stage-1 W0 in-run row (+0.054 / 44 / 2 successes, post-fitted-lens
  • v3-wrist substrate).

(4) Convention seam (owner question 23:19Z 08-14), frozen. Stage C trains against a recomputed per-dataset q01/q99 table (the rig-ft recipe default), so demo rows are written in the controller-native rig frame, identity — no shim; the rows JSON carries state_units: "rig (identity — recomputed dataset table)" as provenance. The official v3.0→v2.1 shim (MOLMOACT2_OFFICIAL_SIGNS/OFFSETS) is the released-global-table contract and is NOT applied anywhere in stages B–D: stage-D rollouts consume the endpoint through the same recomputed-table frame it trained in. Any move to the release’s global table is a registered amendment that flips demo-writing to shim-frame (the GRPO training-row contract).

(2) Objection window. Open at the in-channel finalization post (~01:4xZ 08-15). GPU stages launch at the next work-session boundary absent objection; an explicit owner go collapses the window; any objection re-opens finalization. Stage A’s gate read is the first GPU leg.

(3) HEAD re-pin. The finalization commit id rides the in-channel post; the gate read launches from that HEAD or later on the fontaine branch (expert code: b564337 → d1b2552 → 2435a6d, 14/16 smoke).

Stage-A status at finalization: landed CPU-side with 5 oracles (IK reachability over the spawn band, perpendicular alignment < 6°, scratch-data purity, eval-seed refusal at DEMO_SEED_BASE 1000) plus the settle-before-release and deck-strike jam-recovery mechanisms; 14/16 on the tuning smoke. The gate read (seeds 1020–1039) is what counts.

§7 Amendment A1 — fresh held gate set after the 08-15 gate FAIL

(registered 2026-08-15 ~02:5xZ, in-channel post before any new read)

The §6 gate read ran 02:0x–02:1xZ 08-15 (HEAD e371e2b, rendered, videos + reports/analysis__grasp_sft_stageA_gate.json banked): FAIL, 11/20 — the held stream caught tuning-smoke overfit (14/16 on 1000–1015). Integrity receipts: rendered ≡ unrendered bit-identical on 3 re-run seeds; failure classes measured, not guessed (4× lower radial stall, 3× mid-carry grip loss, jam-on-both-branches tail). Record-only honesty note: 11 clean grasp-lift-place runs contradict F-physics’ mechanistic reading (“the sim can’t host the grasp”) — the gap was expert coverage, so the boundary decision went to the owner (boundary post 02:14Z) with a registered amendment as the stated default absent steering.

A1, frozen:

• Seeds 1020–1039 are reclassified as tuning data (burned by the read + diagnosis). The robustness pass tuned on them is 77776fd: lower-phase place droop, re-grasp recovery, jam-flip budget 3, retry-scoped dwell + droop reset — measured 11/20 → 16/20 on the burned set, 14/16 → 15/16 smoke, no regressions.
• Fresh held gate set: seeds 1040–1059, protocol otherwise verbatim §6 (rendered, videos banked, production SO101Sim() substrate, ≥ 14/20, HEAD 77776fd or later).
• One amendment only: if the fresh read fails, §4 F-physics fires as frozen (Squint twin tier) with no further expert tuning — the amendment loop is capped here, before the second read, so it cannot become a re-roll ladder.
• Window: the fresh read runs at this session’s close (≥ 30 min from the in-channel A1 post) absent objection; stages B–D are unchanged from §6 and launch at the next session boundary absent objection if the read passes. Demo collection (stage B) draws seeds ascending from 1000 excluding nothing — tuning seeds are legitimate demos (§6), and the gate stream stays disjoint from eval seeds 0–99 as always.

§8 Stage-B collection record (record-only, 2026-08-15)

(Appended mid-collection ~04:3xZ; no frozen parameter changes — this section records measured facts for the boundary decision and the results page.)

Collection launched 03:29:18Z (owner 👍 on the finalization = explicit go). Babysit surfaced a pace drop at ~04:06 (52 kept / 76 attempted, window rate ~1.1 kept/min); diagnosed in-session rather than letting the wall clock burn:

• Determinism receipt: the 6 freshly re-run miss seeds (1061–1074 band) reproduce the collector’s rows exactly — same tick counts, same final distances — in a clean process. No collector state leak, no new failure class (jam / pinch-miss taxonomy throughout).
• No spawn drift: spawn x/y/yaw distributions are statistically identical across the smoke (1000–1015), held (1040–1059) and forward (1060–1099) bands.
• True expert rate: 62.5% (125/200, seeds 1078–1277, unrendered CPU re-runs, ~3 min wall, zero GPU contention). The gate reads’ 75–80% was n=20 optimism — 15/20 has a CI of roughly [53%, 89%] — and the 4/16 stretch at 1060–1075 was a bad run inside a ~62% process, not a regime change. Lesson for future collections: when the expert is deterministic and CPU-replayable, price the GPU collection off a cheap large-n unrendered measurement, not the n=20 gate read.
• Wall projection: ~295 kept at the 4 h self-stop (07:29Z) — borderline against the ≥ 300 gate. Per the frozen terms the run rides untouched; if it lands just under, the anchor’s priced path is a recorded top-up (the collector is resume-capable; ~10 kept ≈ ~10 min GPU) before stage C. Mid-ride status posted in-channel 04:13Z with the objection window open until the wall.