Candidate–Priority Coupling in Discrete-Diffusion Robot Policies

Successful robot execution does not reveal whether a policy can recover multiple valid behaviors from the same state. We identify a source of coverage loss in discrete-diffusion decoding: ranking token positions by the probability of the sampled candidates that will be committed. An exact selection identity shows how this candidate–priority coupling can distort the output distribution even with exact conditionals. Sample- split confidence ranks positions with an independent pilot draw and commits a separate draw, removing this dependence. On ForkBench-Choice, a controlled manipulation task, matched two-draw interventions in the first policy query hold weights and model-call budgets fixed. Sample splitting improves primary-fit Coverage@10 by 20.4 percentage points (95% paired-state bootstrap interval: 11.1–29.6), while satisfying a prespecified validity guard. A second fitted checkpoint shows a consistent gain, with disclosed technical qualifications. A separately trained VLA also shows increased diversity on a fresh Approach-Side panel when choosing between prevalidated fixed-controller routes. Together, the exact identity and matched no- remasking interventions identify candidate–priority coupling as a source of finite-budget behavioral coverage loss. Decoding rules determine which valid behaviors a fixed policy recovers and warrant evaluation alongside task success.