标题： 生成式大规模预训练模型用于自动广告竞价优化 显示英文标题

标题： Generative Large-Scale Pre-trained Models for Automated Ad Bidding Optimization

摘要： 现代自动出价系统需要在整体性能与多样化的广告主目标和现实世界约束之间取得平衡，反映了行业动态和不断演变的需求。 近年来，条件生成模型（如变压器和扩散模型）的进展使得可以根据广告主偏好直接生成轨迹，为传统的基于马尔可夫决策过程的方法提供了一个有前景的替代方案。 然而，这些生成方法面临重大挑战，例如离线和在线环境之间的分布偏移、动作空间探索有限以及必须满足诸如每千次展示费用（CPM）和投资回报率（ROI）等约束。 为了解决这些挑战，我们提出了GRAD（基于专家混合的生成奖励驱动广告出价），这是一个可扩展的自动出价基础模型，结合了用于多样化出价动作探索的动作专家混合模块，以及用于约束感知优化的因果变压器价值估计器。 大量的离线和在线实验表明，GRAD显著提高了平台收入，突显了其在解决现代广告主不断变化和多样化需求方面的有效性。 此外，GRAD已在美团的多个营销场景中实现，美团是全球最大的在线外卖平台之一，导致商品交易总额（GMV）增加了2.18%，投资回报率（ROI）增加了10.68%。 显示英文摘要

摘要： Modern auto-bidding systems are required to balance overall performance with diverse advertiser goals and real-world constraints, reflecting the dynamic and evolving needs of the industry. Recent advances in conditional generative models, such as transformers and diffusers, have enabled direct trajectory generation tailored to advertiser preferences, offering a promising alternative to traditional Markov Decision Process-based methods. However, these generative methods face significant challenges, such as the distribution shift between offline and online environments, limited exploration of the action space, and the necessity to meet constraints like marginal Cost-per-Mille (CPM) and Return on Investment (ROI). To tackle these challenges, we propose GRAD (Generative Reward-driven Ad-bidding with Mixture-of-Experts), a scalable foundation model for auto-bidding that combines an Action-Mixture-of-Experts module for diverse bidding action exploration with the Value Estimator of Causal Transformer for constraint-aware optimization. Extensive offline and online experiments demonstrate that GRAD significantly enhances platform revenue, highlighting its effectiveness in addressing the evolving and diverse requirements of modern advertisers. Furthermore, GRAD has been implemented in multiple marketing scenarios at Meituan, one of the world's largest online food delivery platforms, leading to a 2.18% increase in Gross Merchandise Value (GMV) and 10.68% increase in ROI.