Transport Discrepancy as a Reliability Signal for Vision-Language-Action Models

Vision-language-action (VLA) models that generate contin-uous action chunks via flow matching lack an internal signal for judgingwhether a given prediction is reliable. Distribution shift and long-horizonrollouts can push backbone representations away from the region the ac-tion head decodes reliably, yet the policy has no mechanism to detector react to this drift. We observe that the cost of transporting observa-tion features to the action representation in a shared feature space risesprecisely when such drift occurs, providing a per-step reliability esti-mate without extra supervision. Building on this observation, we proposeDiG (Discrepancy Gate), a lightweight plug-in module for flow-matchingVLA policies. DiG computes a sliced Wasserstein transport cost betweenbackbone features and the action expert’s own input projection, maps itthrough an exponential gate, and uses the gate to modulate both a resid-ual feature refinement and the training loss. At inference time, the gateenables DiG-Refine, an iterative refinement process that corrects actionchunks before execution. Experiments on both simulation and real-worldscenarios show that DiG consistently improves success rates, with thelargest gains under distribution shift and on long-horizon tasks.