这篇论文提出一个很实用的思路：训练生成模型时直接考虑下游决策成本，而不是只匹配数据密度。在概率预测场景中特别有价值。
该论文提出决策感知训练方法，在基于样本的生成模型训练中，将能量分数（energy score）目标与可微决策损失（decision loss）结合，直接惩罚下游决策成本。决策损失本身是严格适当的评分规则，具有理论保证。在1个合成任务和2个真实世界任务上验证，该方法在成本敏感区域提升性能，同时保留完整概率预测。
Sample-based generative models are increasingly used for probabilistic forecasting in high-stakes decision settings, yet their training objectives are blind to the decision maker's cost structure. These models are commonly trained with strictly proper scoring rules, such as the energy score, which allocate their training signal in proportion to data density, with no awareness of where forecast errors are most costly for downstream decisions. We therefore propose decision-aware training for sample-based generative models, augmenting the energy score objective with a differentiable decision loss that directly penalises the cost incurred by acting on the model's forecast. This combined loss is theoretically grounded, as the decision loss is itself a proper scoring rule. We validate our method on one synthetic and two real-world tasks, showing targeted improvements in cost-sensitive regions while retaining full probabilistic forecasts.