TLDR
平均回报相近的策略在罕见故障上的表现可能截然不同，但准确估计下尾条件风险价值（CVaR）通常需要大量高成本 rollout。当随机工作流中不同的条件分量可以被独立查询时，我们研究如何在固定评估预算下分配查询，以最准确地估计固定策略的 CVaR。我们为每个可查询的条件分布推导了一个尾部影响，用以聚合其不确定性在每次 Bellman 复用中对 CVaR 的整体作用；其方差给出了固定设计的效率下界以及对应的 oracle Neyman 分配。尾部影响（Tail-Influence）……Policies with similar mean returns can differ sharply in rare failures, yet estimating lower-tail conditional value-at-risk (CVaR) accurately can require many costly rollouts. When different conditional components of a stochastic workflow can be queried separately, we ask how to allocate a fixed evaluation budget to estimate a fixed policy's CVaR most accurately. We derive a tail influence for each queryable conditional law that aggregates how its uncertainty affects CVaR across every Bellman reuse. Its variance yields the fixed-design efficiency bound and the oracle Neyman allocation. Tail-In