Repeat Before You Route: Evaluating Adaptive Compute for VLA Policies

Adaptive robot compute depends on a simple question: which tasks benefit from a different compute setting? We introduce Repeat-to-Select, a method that measures the opportunity for this choice using repeated rollouts. A repeat-product estimator isolates stable differences in success probability; unbiased centering then separates the advantage of a better default from task-dependent selection opportunity. We derive a sharp upper bound on the gain available to a task-based selector and a local lower bound linking the number of tasks, repeats, and detectable effect size. Together, these results turn repeated outcomes into a procedure for choosing defaults, measuring selection opportunity, and allocating an evaluation budget. On 120 tasks with three repeats per setting, increasing the executed action chunk from one to ten improves success by 5.83 percentage points and reduces policy calls by 91.56%. A separate 360-episode five-action comparison supports ten-action execution with 50.17% fewer calls and a nominal success-loss bound within two points. Solver-depth selection remains statistically unresolved: its nominal ceiling is 4.17 points and its conservative ceiling is 26.72 points. Across 110,610 public code and agent outcomes, apparent oracle gains exceed cross-validated same-task selection gains in all eight panels. The method and released outcomes provide a common basis for improving fixed settings and evaluating task-based compute allocation.