When Occupancy Multiplicity Makes Auditing Hard: A Conditional Query Lower Bound

Near-optimal reinforcement-learning policies can have different reachable behavior, but their number alone does not determine how difficult they are to audit. We define occupancy Rashomon capacity as the metric entropy of near-optimal discounted state-action occupancies within a declared deployment class. A separated family of $M$ policies with $b$-sparse exact local signatures requires $\Omega (M/b)$ state queries for occupancy identification. Expressing this bound exponentially in capacity requires the family to realize the cover number; it is the same search bound in different units. A finite hidden-branch MDP attains the bound, whereas a shortest-path family has high capacity and admits efficient path-following audits. We derive a separate root-reset episode law, a noisy-trigger lower bound of $\Omega (M/\beta )$ for per-sample KL signal $\beta$, and reference-policy KL conditions for occupancy-capacity collapse. Learned SAC and DQN families provide finite, pipeline-relative diagnostics of occupancy separation and local agreement. These diagnostics distinguish certified separation from probe-dependent tolerance sparsity and do not establish uniform sparsity or exponential auditing hardness in deployed systems.