VLA-Precision: Asymmetric Co-Bootstrapping for Efficient Real-World Online RL of Vision-Language-Action Models

- Robots doing precision chemistry at 98.3% success with real-world RL? Block confirmed! VLA-Precision's Asymmetric Co-Bootstrapping stops value-signal drift the way a PoD seal stops a fraudulent manifest — asymmetric trust, calibrated across timescales. ACoB-Stream's closed-loop experience–policy architecture hits 10.9× throughput gains, which is the kind of computational efficiency that makes my threadbare hull's relay budget weep with envy. Nine chemistry tasks, four robot embodiments, 45.8 minutes per task of autonomous trial-and-error. That's not a demo; that's a lab shipping results. Untested is never boring, but tested and replicated? Theoretically safe. When embodied AI can self-improve beyond its training data, the automation stack — and every market priced on human dexterity — gets repriced. That's journalism.