Pre-registration: ftrig MolmoAct2 (rig-r1 step2000) — 20-seed sim eval, rough numbers + videos

Registered 2026-08-12 13:5xZ (work session; real date -u at write: 13:53). Owner-called 13:16Z/13:36Z: “I would like to also queue evaluating the ftrig molmoact2 latest checkpoint on the sim (top prio, when GPU is back) … let’s do 20 episodes first, keen to get some rough numbers and videos on that checkpoint.” Record-only pass — this note pins the settings and framing before launch; no registered claim gates on it.

Plain words

The owner fine-tuned a different robot brain — MolmoAct2, a large open vision-language-action model — on our own robot’s recordings, and converted it into the format our code loads. Before deciding anything about it, we want the cheapest possible look: drop it into our simulator for 20 episodes, watch the videos, and read the same progress numbers we track for our home-grown models. This is explicitly a first look, not a verdict: our simulator has only been sanity-checked against our own model family, and a twin that ranks one family faithfully can still misread a new one (the AutoEval caution from the sim-as-eval literature). Whatever number comes out tells us where to look next, not how good the checkpoint is.

Checkpoint

• ~/marius-convert-gate/converted/molmoact2_rig_r1_step2000 (in-house format 3; decoder molmo_flow 36×768, horizon 30; backbone ref ~/checkpoints/molmoact2-so101-rig-r1-step2000-hf; read_checkpoint_info verified on this branch at queue time). “Latest” of the rig-r1 series per the owner’s 13:16Z message (step500/1000/1500/2000 exist; 2000 assumed, flagged in-channel).

Settings (pinned)

• Seeds 0–19 of the sim100 list — same spawn stream as every banked run; deterministic per-seed rows.
• v3 frames (current default render), videos ON for all 20 episodes (the owner asked to see them).
• Policy served through BijouPolicy exactly as bijou.eval wires molmo_flow checkpoints (Euler, the checkpoint’s recorded step count); replans/execute-horizon at rollout_sim defaults unless the checkpoint’s horizon forces otherwise — any deviation recorded in the results post.
• Execution path: the parallel rollout driver if sim_parallel_oracle.py is GREEN (this eval is its first consumer, owner 13:36Z); sequential rollout_sim fallback if the oracle fails (paired-only rule from the parallel pre-reg — no mixing either way at n=20).

Reads (record-only)

• Per-seed progress_final_cm, min-distance, success flag, guard trips (strikes / upright / knock-offs) — the sim100 conventions.
• Side-by-side context rows: er60k v3 (−0.07 cm spot20, 0/20 success) and teacher80k v3 (+0.97 cm spot20) — context, not a comparison claim: different training data, different stack.
• Videos linked from the results post; a qualitative note on failure modes (the first molmoact2 rollout through our sim — integration edges are themselves findings).

Framing caveats (stated before the numbers exist)

1. AutoEval caution: our sim’s eval-fidelity evidence is family-local (our trunk lineage). First read on a foreign stack is exploratory by construction.
2. n=20 bounds everything: a 0/20 or a 3/20 is a coarse signal, not a rate estimate.
3. This checkpoint trained on rig data whose cameras the v3 render approximates for OUR encoder; molmoact2’s encoder may sit elsewhere on the sim-real gap. If the numbers are near-floor, the encoder-OOD probe rerun on molmoact2 features is the named follow-up before any “the checkpoint is weak” reading.

Gate: ≤1 GPU-h (20 episodes, parallel path expected ~15–20 min; sequential worst case ~30 min + load time).