ActionX — pre-train the expert first, then unfreeze everything

Frontiers in Neurorobotics, 2026 — 10.3389/fnbot.2026.1806605. Read 2026-08-09 (work-session lit slice, the day the attach screen’s Δ_seam readout lands). Venue caveat up front: Frontiers, not a first-tier robotics venue — we weight the ablation SHAPE, not the absolute numbers.

Plain words. When you bolt an action module onto a big vision-language model, you have to decide what learns when. This paper’s recipe: first train ONLY the small action module (the big model stays locked), then — once the action module is competent — unlock everything and train the whole stack together. Their twist is doing that first stage with reinforcement learning (trial and success/failure) instead of pure imitation. The striking part isn’t the RL: it’s the ablation showing that skipping the first stage entirely makes the joint stage bad or useless, while ANY competent pre-training of the action module first makes the joint stage work. That “warm-start the expert, then go joint” pattern is exactly the escalation rung our attach screen has queued behind tonight’s frozen-vs-joint readout.

What they built

• Stack: InternVL3-1B trunk (InternViT-300M vision), an 8-layer self-attention flow-matching action expert with sinusoidal time encoding. Conditioning = trunk hidden state concatenated with a separately-embedded robot state (state kept OUT of the multimodal fusion — an independent MLP path).
• Stage 1 — expert pre-training, trunk FROZEN: PPO on the expert only, sparse 0/1 success reward, critic warmed on 10% of the demo data; ~5.7k–14k RL steps depending on task family.
• Stage 2 — joint SFT, everything UNFROZEN: full-parameter fine-tuning on demonstration velocity fields, no stop-gradient, no insulation. They report faster convergence because the expert arrives pre-initialized.

Experiments

LIBERO (4 suites × 10 tasks): 91.5% average; Meta-World (50 tasks): 81.3% (+13 over their prior-work line); real UR12E single-arm + a dual-arm platform (+16% average over baselines there). Baselines: Diffusion Policy, OpenVLA, π0, π0-FAST, SmolVLA, TinyVLA. The gains concentrate on LIBERO-Long (+6 vs SmolVLA); Spatial/Object/Goal are modest.

The ablation that matters to us (their Table 2, expert pre-training regime → suite success):

(The 0% row reads suspiciously absolute — likely a matched-budget snapshot rather than convergence; we treat the ORDERING as the finding, not the magnitudes.) RL adds +14 on Long over supervised pre-training, but the cliff is none→any: +38 to +92 points.

What transfers to us

• The F-then-joint rung gets published support in its exact shape. Our banked F-then-joint escalation (warm-start a joint run from the F arm’s converged expert) is literally their supervised row: frozen-trunk expert SFT first, then full unfreeze. Their supervised-pre-train→joint beats joint-from-scratch by +38 (Long) — independent corroboration, on a pretrained-VLM trunk, for APT’s grid. Ledger entry added to idea #4.
• No-insulation joint stage: their stage 2 unfreezes everything with no stop-grad — consistent with APT’s finding that WITH a competent expert, plain joint training is the best published recipe. If tonight’s Δ_seam readout says F≈K, this is one more vote that the next discriminating contrast is initialization, not seam plumbing.
• State kept out of the fusion (separate MLP path, concatenated late) — same family as our prompt-side state token; nothing to change, mild corroboration.

What does NOT transfer

• The RL half needs rollouts. Sparse-success PPO presumes a resettable simulator or cheap real resets; our panel is offline MAE against teleop data. Their RL-vs-supervised margin (+14 Long) is real but unreachable for us today; the reachable part is the supervised row — which is the F arm we already trained.
• No frozen-vs-joint readout at matched conditions. They never ablate freezing the trunk in stage 2, so this paper does NOT re-rank F-vs-K — it informs only the escalation rung behind the readout.
• Venue + benchmark caveats: Frontiers; LIBERO/Meta-World sim suites with saturation effects; success-rate metrics, not our paired-per-frame MAE reads.

Fed

• #4 (attachment): F-then-joint pre-reg draft (idea4-f-then-joint-prereg-draft, opens at the Δ_seam readout) gains its second independent citation for warm-start-then-joint; APT’s condition (pretrained expert ⇒ no stop-grad joint wins) now has a same-shape replication on a 1B-class trunk.
• #16 (post-SFT menu): the RL-on-expert-only pole (their stage
  1. is a cheaper cousin of FlowPRO — expert-scoped, trunk-frozen — banked as a note on the menu’s weight-space side; retention still unmeasured in both.