#Tsinghua AIR (4)

Zetta ζ: Closed-Loop Self-Evolution for a Frozen VLA Policy

Tsinghua AIR and Z-Trans AI present Zetta ζ, a closed-loop embodied harness that improves a frozen VLA policy without a single gradient update. Instead of fine-tuning, it evolves the execution harness around the policy: code-based runtime critics watch every action, recovery skills take over on deviation, and a validation gate admits only skills that generalize. The frozen baseline scores 31.0% on LIBERO-Pro; the same policy under Zetta ζ reaches 92.5% (+56.3 absolute points), with a +20-point gain to 93.6% across 18 RoboCasa tasks. Z-Infra scales valid rollout throughput 20.6× and speeds inference 11.1×. Skills transfer zero-shot, and clear robotic "aha moments" emerge.

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