Guidance under Shift: Deployment-Aligned Semantic Interfaces for Vision–Language–Action Policies

Explicit chain-of-thought (CoT) can improve vision–language–action (VLA) policies by exposing task progress and spatial intent, but guidance available during training is often more reliable and temporally aligned than online predictions at deployment, which may be incorrect, missing, or outdated. This mismatch can make a policy trained with reliable guidance brittle at deployment. Our flow-matching analysis bounds the deployment loss by training error, guidance-distribution coverage, and conditional drift, motivating two training principles: covering deployment-time guidance conditions and exposing the policy to temporal staleness. We therefore train a VLA policy with subtask descriptions, affordance regions, and image-space trajectories drawn from annotated, generated, corrupted, and missing guidance, while varying their physical age—the elapsed time from the source observation to policy use. Current observations and action supervision remain aligned. An asynchronously refreshed dependency graph overlaps reasoning and action generation while preserving an explicit and inspectable CoT interface. Controlled evaluations vary guidance reliability and physical age. We evaluate closed-loop success on LIBERO and on training and testing tasks of RoboTwin 2.0, as well as a real-world “desktop-cleaning” task and its variants, demonstrating robustness and generalization on physical robots.