CommitFlow: Execution-Aligned Sampling for Receding-Horizon Robot Policies

Flow-based robot policies jointly generate an action chunk, but receding-horizon controllers execute only a prefix before replanning and discard the remaining actions. With bidirectional attention across action tokens, noise assigned to this discarded tail can nevertheless influence the actions sent to the robot. We call this dependence tail-noise leakage and introduce tail-resampling sensitivity (TRS) to measure how the executed prefix changes when only tail-local noise is resampled. To remove this dependence, we propose CommitFlow, which aligns the sampler’s action-token dependencies with the controller’s execution boundary. Its strict formulation isolates every prefix from subsequent position-local noise, while CommitFlow-K preserves bidirectional coordination within a known execution block and prevents influence from the discarded tail. A shared stochastic context supports chunk-level coordination without reopening tail-to-prefix paths. We prove counterfactual prefix invariance under both dependency designs while retaining parallel action sampling. Controlled interventions demonstrate that tail-noise leakage can affect closed-loop outcomes: resampling the full discarded tail produces both success and failure for the baseline in 6 of 40 fixed states, whereas CommitFlow remains invariant. Beyond this mechanism study, the full model improves aggregate success on LIBERO (+1.18 pp), RoboTwin (+3.92 pp), and AgiBot G1 tasks (+2.57 pp), while reducing across-replan inconsistency. These results show that execution-aligned sampling can eliminate a behaviorally consequential dependence on discarded-tail randomness while maintaining or improving aggregate task performance.