Beyond Noise: Regulating How Much of the Prior Survives in Flow-Matching Robot Policies

Most flow-matching robot policies generate actions from Gaussian noise, ignoring the temporal continuity available in robot interaction. Recent methods warm-start the flow with informative sources such as previous actions or visual forecasts, implicitly assuming that a better source produces a better action. We show that this assumption is incomplete: informative sources help, but much of their information is attenuated by the flow before it reaches the executed action, so what must be controlled is how much of the prior survives, not only which prior enters. We therefore propose Beyond Noise. It enters the flow at a state that couples the prior’s weight, the injected noise and the remaining path through a single entry time q. Beyond Noise selects a default entry on validation data and applies the regulator’s per-call refinement where validated, dynamically adjusting q according to the current state; the regulator learns from counterfactual closed-loop returns. A patch-token memory lets the policy use where objects are once the prior is kept. On four LIBERO suites, under a protocol that makes every choice on held-out initial states, Beyond Noise reaches 81.0% average success with a single function evaluation, above matched Gaussian, visual-prior and external baselines, with the largest gains on long-horizon tasks. Exploiting a better prior requires deciding how much of it survives.