Rerouting the Flow: Versatile Test-Time VLA Adaptation via Source Distribution Learning

Vision-language-action (VLA) agents acquire broad manipulation capabilities from large-scale robot data, yet remain brittle under distribution shifts at deployment. Our diagnostic analyses show that failed rollouts can retain task-directed behavior and that denoising perturbations occasionally yield success, suggesting that some failures reflect unreliable access to pretrained capabilities rather than their absence. Motivated by this insight, we propose ReFlowTTA, a versatile test-time adaptation framework that learns the source distribution of initial noise of a flow-based VLA, shifting probability mass toward states associated with better execution outcomes. Task feedback and optional human corrections guide this shift, allowing accumulated experience to shape subsequent sampling and make successful behaviors more accessible. This turns adaptation across objects, scenes, and robot embodiments into a common problem of learning where generation should begin, without relearning how actions are produced. Across three simulation benchmarks and two real-world robot platforms, ReFlowTTA consistently outperforms representative test-time adaptation methods with limited deployment interactions. Further analyses show that adaptation stabilizes and that the gains depend on the direction of the learned source shift instead of on generic perturbations. We release the core implementation at an anonymous repository.