Conditional generative models are increasingly used as scenario generators for stochastic optimization, but standard training objectives emphasize uniform distributional fit rather than the downstream decisions induced by generated scenarios. This creates an objective mismatch: errors in statistically common regions may have little effect on decision regret, whereas errors in decision-sensitive regions can substantially change the optimal action. We propose Decision-Weighted Flow Matching (DW-FM), a regret-aligned training framework that preserves the simplicity of standard flow matching while reweighting its velocity-regression objective using decision-sensitive endpoint information. Theoretically, we connect downstream regret to pathwise velocity mismatch through a loss-induced decision discrepancy and an adjoint transport argument, yielding an ideal regret-aligned surrogate and practical endpoint-weighted objectives with regret guarantees. Empirically, we demonstrate the effectiveness of DW-FM on three CVaR-based contextual stochastic optimization benchmarks spanning synthetic portfolio, semi-real financial, and traffic-CVaR tasks, where DW-FM improves downstream regret over standard baselines.

翻译：条件生成模型在随机优化中越来越多地被用作场景生成器，但标准训练目标侧重于均匀分布拟合，而非生成场景所引发的下游决策。这导致了目标不匹配：统计常见区域的误差可能对决策遗憾影响甚微，而决策敏感区域的误差则会显著改变最优行动。我们提出决策加权流匹配（DW-FM），这是一种与遗憾对齐的训练框架，它保留了标准流匹配的简洁性，同时利用决策敏感的端点信息对其速度回归目标进行重新加权。在理论上，我们通过损失诱导的决策不一致性和伴随输运论证，将下游遗憾与路径速度不匹配联系起来，从而得到理想的遗憾对齐替代目标以及具有遗憾保证的实用端点加权目标。在实验上，我们在三个基于CVaR的上下文随机优化基准任务（涵盖合成投资组合、半真实金融和交通CVaR）上验证了DW-FM的有效性，结果表明DW-FM相较于标准基线显著改善了下游遗憾。