这篇论文给出了一个训练生成模型的新思路——DW-FM，专门优化下游决策效果，在CVaR任务上比普通流匹配更好用。
论文提出决策加权流匹配（DW-FM）框架，通过重加权速度回归目标对齐下游决策遗憾。理论证明通过损失诱导的决策差异和伴随传输论证，下界遗憾可关联到路径速度不匹配。在合成投资组合、半真实金融和交通CVaR三个基准上，DW-FM相比标准流匹配显著降低下游遗憾。
Decision-Weighted Flow Matching for Contextual Stochastic Optimization
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.