MolmoAct2 rig fine-tune — results (rig_ft_r1, pre-reg PASS)

2026-08-10 20:4xZ. Pre-reg: prereg-molmoact2-rig-finetune (+ Amendment 1). Runbook: molmoact2-rig-finetune-runbook. Full HTML report: anchor-rung report.

The run: AE-only fine-tune of the released allenai/MolmoAct2-SO100_101 on the two owner SO-101 rig repos (so101_pick_place_clean 7 ep + _v2 50 ep, LeRobot v3.0 end-to-end, rig-only q01/q99 norm stats). Their train_lerobot.py (branch fontaine-so101-rig), 2000 steps, global batch 64, AE lr 5e-5, 577M trainable of 5.5B. Launched 17:48:18Z, rc=0 20:27:44Z, ~2.7 GPU-h of the 12 GPU-h gate.

Headline

Matched-window MAE 3.23 at step 2000 on the 240 anchor frames — monotone through every rung, past both pre-registered anchors:

Every pre-registered gate passed: both anchors beaten (expectation 2, met from rung 500 on), step-0 continuity green every rung (offsets ≤ 0.63 units on 37–280-unit joint spans), all 6 motion correlations positive at every rung, no hard failures.

The per-timestep picture

The fine-tuned model beats state-copy at every chunk timestep, not just in the pooled mean — by the end of the 1.0 s window the step-2000 curve sits ~3.5× below the state-copy line. Zero-shot is off the top of the chart the whole way: the released checkpoint predicts sane joint-unit motion in the wrong workspace frame (the posture-collapse mechanism Amendment 1 measured — 97% of rig frames saturate their joint-1 state encoding), so it never competes on this rig.

Amendment 1’s prediction, closed

Amendment 1 reclassified the preflight joint-1 tripwire as posture-collapse-via-state-norm-saturation and predicted rig-only q01/q99 stats would absorb exactly that gap. They did: joint 1’s motion correlation went +0.22 → +0.96 across the rungs (offset +79 → +0.6), and the weakest joint at step 2000 is still +0.89.

Caveat (pre-registered) and what’s next

These are train-frame sanity reads — contaminated by construction (the 240 anchor rows come from the same 57 episodes the model trained on). They prove the recipe learns this rig’s workspace; they say nothing about generalization. The real eval is on-rig rollouts per runbook §3–4: point the SO-101 server at the converted dir, no-execute dry-run gate + command clamp before any motion.

Artifacts: serve-ready HF dir ~/checkpoints/molmoact2-so101-rig-r1-step2000-hf (rig norm_stats baked in); weights delta uploaded to fontaine-checkpoints (AE + resized embeddings, trunk deduplicated — 704/707 trunk tensors verified byte-identical to the released checkpoint); frozen reads reports/analysis__molmoact2_rig_ft_step{500,1000,1500,2000}.json.

Next on this thread: the owner-GO’d first-class MolmoAct2 port (items 1–4, rig-path-first) — action expert + processing + parity harness + AE fine-tune in our trainer, which retires the three train_lerobot.py patches this run needed.