FlowShift: Risk-Aware Test-Time Scaling for Vision-Language-Action Models with Verifier-Driven Guidance and Selection

Vision-Language-Action (VLA) models often degrade under test-time distribution shifts, when training and deployment distributions diverge. Existing test-time scaling methods mainly select among completed action candidates, leaving the generation process constrained by pretrained flow dynamics and the verifier fixed under deployment changes. To address these limitations, we propose **FlowShift**, a plug-and-play, risk-aware test-time scaling framework that adaptively shifts the action distribution while keeping the VLA backbone frozen. (1) FlowShift first introduces a **Test-Time Adaptive Task-Progress Verifier**, which adapts latent prompts through self-supervised state prediction to remain responsive to deployment changes. The adapted verifier provides two complementary signals for subsequent action generation: reward gradients for guidance and scalar rewards for evaluation. (2) Building on these signals, **Verifier-Guided Stochastic Flow Sampling** converts selected ODE updates into verifier-guided SDE transitions, using reward gradients to steer intermediate states toward higher-reward regions and scalar rewards to select promising particles. This process progressively shifts the action distribution toward a reward-aligned target. (3) To prevent excessive distribution shifts, FlowShift further introduces a **Risk-Aware Router** that models multi-timestep flow features and trajectory history to estimate execution risk. It activates stochastic flow sampling for high-risk action chunks while retaining the original ODE path for low-risk ones, thereby controlling distribution shift. FlowShift improves ${\pi }_{0.5}$ from 83.9% to 85.0% on LIBERO-Plus, from 63.9% to 68.9% and 39.0% to 46.2% on RoboTwin 2.0 under the *Easy* and *Hard* settings, respectively, and from 56.7% to 69.2% in real-world manipulation.