Ten operator-judged trials are fine. An autonomous loop that records hundreds of rollouts and fine-tunes on the good ones is not, because every success it mislabels trains the policy on a failure. This is the day the judge became the most important component in the stack, and the day a published 4B reward model lost to 40 lines of OpenCV.
If a world model cannot rank actions, maybe it can tell how far along a task is: distance in latent space to the goal frame should fall as the arm gets closer, and a stall is when it stops falling. Zero labels needed. G6a tested that; G6b tested a hierarchical version four days later. Both failed the same way.
G1b asked the question that decides whether a latent world model can plan on this arm: given the expert chunk and seven decoys, does imagining each one rank the expert first? Two world models, one bigger pretrained one, and a baseline with no model at all. The baseline won.
ACT is the most common SO-101 policy and vla.cpp did not have it. Merged as vla.cpp #38 after a scoping issue (#37) the maintainer approved in a day. The pitch was never speed: PyTorch ACT already runs at 59 ms per 100-action chunk on the Orin. The pitch is no PyTorch at inference: no 5.5-hour source build, no 12–24 GB image, and CPU-only boards can run it.
LeWM (the stable-worldmodel recipe: a JEPA trained end to end from pixels, no reconstruction) on the 182 bowls episodes, two camera views, 100 epochs. Gate G0 asked whether the model is real before anything is built on it.
Ten days of work, several variants, a published PyPI package, and a closed project. TurboVLA fine-tuned on the two-task bowls data learned to perform whichever task it had seen last, whatever the instruction said. The failure taught me two things about data that the later datasets were built around.
Corrected 2026-10-23
The conclusion below about the model was wrong. My port differed from the paper in four places, and the paper's own trainer, run on the same data with the paper's initialisation, also ignores the instruction (task swap 0.020° vs 0.105 for the same model on LIBERO-Goal). The data finding (fact 7) is the whole story. Full account: Paper vs hardware 12: TurboVLA.
The first world-model idea was the obvious one: let the policy propose several action chunks, let a latent world model imagine each one, keep the best. Before building any of it, I checked whether the premise held. It did not.
vla.cpp (the llama.cpp-style engine for vision-language-action models) had benchmark reports for the Orin Nano Super and the AGX Orin, and nothing in between. The Orin NX 16 GB is the board most hobby arms end up on. This report fills the gap and is merged upstream as vla.cpp #36.
974 ms per chunk made SmolVLA stutter on the Orin NX. Serving the same checkpoint through vla.cpp brought it to 238 ms with no loss in task success.
Before any research question, the arm had to run a learned policy reliably and I had to be able to measure it the same way every time. This post is the baseline everything else is compared against.