这篇论文教你用历史A/B数据+生成模型筛选创意，50臂实验最高提升45%，比人类写的强很多。
该研究提出一种离线到在线的广告创意优化流程，利用生成模型产出候选创意，并通过基于历史A/B测试数据训练的预测模型在推理时进行排序筛选。最终测试集在在线自适应实验（50臂）中验证，最佳创意比人类作者创意提升45.1%互动率，另两个实验分别提升46.7%和36.2%。尽管预测模型无法直接选出最优创意，但能有效引导生成模型产出强候选，再通过自适应实验高效评估。
Offline-to-Online Creative Optimization with Generative Models and Adaptive Testing
Ad creative optimization is increasingly constrained by evaluation rather than generation. Generative models can produce many plausible creatives, but reliable evaluation requires online experiments, in which only a limited slate can be tested. We study how to use data from historical A/B tests to generate and select the candidates in that slate. We developed and deployed a performance-driven offline-to-online workflow that guides creative generation with a predictive model as an inference-time critic. In the offline phase, we use a predictive model trained on historical experiments to rank and refine variants created by a generative model. A final test slate is then deployed in an online adaptive experiment. In a 50-arm field experiment, we found that the best creative generated with this method yielded 45.1% higher engagement than the best human-authored creative. Two additional experiments showed the same upper-tail pattern, with lifts of 46.7% and 36.2%. We found that despite the predictive model being too noisy to directly identify the best creative offline, it effectively guides the generative model toward creating strong candidates that can be efficiently evaluated in an adaptive experiment. The results suggest a design principle for creative optimization with generative models: use predictive models to guide generation of a slate to test, judge the slate by whether it contains high-performing candidates at a feasible test size, and use adaptive experiments to select among candidates while limiting traffic lost to weak arms.