ReflectVLA: Event-Anchored Paired Trajectory Conditioning for Corrective Re-execution

Vision-Language-Action (VLA) methods have achieved strong performance for robotic manipulation. However, models trained on success-only datasets often fail when driven into out-of-distribution states. Existing correction strategies use different intervention interfaces, while paired trajectory conditioning directly represents the deviation between failed and successful behaviors for controllable re-execution. In this paper, we propose ReflectVLA, a framework that learns corrective re-execution from paired successful and failed trajectories. The core of ReflectVLA is an event-anchored Contrastive Trajectory Encoder (CTE) that learns paired trajectory representations, capturing how failed trajectories diverge from successful references. The deviation representation is encoded as correction tokens and injected into the VLA inference process. A correction adapter enables the policy to interpret these correction tokens and generate re-execution actions while preserving the base VLA behavior during normal execution. During inference, an external detector identifies an incipient failure. When the trigger arrives before an irreversible environmental change, ReflectVLA interrupts execution, rolls back to a visited pre-failure keyframe, and retrieves the most relevant successful reference from a Success Bank. The paired success–failure trajectory is jointly encoded to guide correction. Experiments demonstrate that ReflectVLA enhances recovery performance under this early, reversible intervention setting.