Adaptive Replanning for Vision-Language-Action Policies via Overlap Disagreement

Modern vision-language-action (VLA) robot policies commonly use action chunking, where the VLA predicts and executes a sequence of future actions before performing inference again. While action chunking improves computational efficiency and temporal coherence, it introduces a fundamental tradeoff: frequent replanning reduces open-loop execution drift but increases inconsistency between consecutive plans, whereas infrequent replanning improves temporal consistency at the cost of accumulating stale execution error. In this work, we model this tradeoff using a nonparametric class of surrogate costs and show that it induces an optimal replanning horizon. We then show empirically that weighted overlap disagreement, an observable quantity that measures the disagreement between overlapping segments of consecutive action chunks, has a minimizer that serves as a reliable predictor of this optimal replanning horizon. Building on this insight, we propose disagreement-guided replanning (DGR), an online adaptive replanning strategy with negligible computational cost that dynamically adjusts the replanning horizon during inference without additional training. DGR is model-agnostic, easy to deploy, requires minimal tuning, and uses a single set of hyperparameters across all models and benchmarks. Across multiple VLA architectures and benchmark suites, our method improves robustness and long-horizon task performance over baselines. Code will be made available upon acceptance.