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

arXiv: https://arxiv.org/abs/2508.02002 · HTML: https://mlsanity.com/paper/2508.02002/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in MoE theory and gating functions, one of 12 directions in Mixture-of-experts.
- Builds on 30 of its 60 references that are in the map.
- First posted on arXiv 4 Aug 2025.

## Placed in

- Mixture-of-experts / [MoE theory and gating functions](https://mlsanity.com/moe/moe-theory-and-gating-functions/)

## Builds on (most cited first, 30 of 30)

- [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://mlsanity.com/paper/2101.03961/) (2021)
- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://mlsanity.com/paper/2106.01345/) (2021)
- [Offline Reinforcement Learning with Implicit Q-Learning](https://mlsanity.com/paper/2110.06169/) (2021)
- [Off-Policy Deep Reinforcement Learning without Exploration](https://mlsanity.com/paper/1812.02900/) (2018)
- [DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models](https://mlsanity.com/paper/2401.06066/) (2024)
- [Efficient Online Reinforcement Learning with Offline Data](https://mlsanity.com/paper/2302.02948/) (2023)
- [The Impact of Positional Encoding on Length Generalization in Transformers](https://mlsanity.com/paper/2305.19466/) (2023)
- [IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies](https://mlsanity.com/paper/2304.10573/) (2023)
- [Auxiliary-Loss-Free Load Balancing Strategy for Mixture-of-Experts](https://mlsanity.com/paper/2408.15664/) (2024)
- [Constrained Decision Transformer for Offline Safe Reinforcement Learning](https://mlsanity.com/paper/2302.07351/) (2023)
- [Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising](https://mlsanity.com/paper/1802.09756/) (2018)
- [Sustainable Online Reinforcement Learning for Auto-bidding](https://mlsanity.com/paper/2210.07006/) (2022)
- [GeRM: A Generalist Robotic Model with Mixture-of-experts for Quadruped Robot](https://mlsanity.com/paper/2403.13358/) (2024)
- [AuctionNet: A Novel Benchmark for Decision-Making in Large-Scale Games](https://mlsanity.com/paper/2412.10798/) (2024)
- [Auto-bidding and Auctions in Online Advertising: A Survey](https://mlsanity.com/paper/2408.07685/) (2024)
- [MoE-Loco: Mixture of Experts for Multitask Locomotion](https://mlsanity.com/paper/2503.08564/) (2025)
- [Generative Auto-Bidding with Value-Guided Explorations](https://mlsanity.com/paper/2504.14587/) (2025)
- [A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising](https://mlsanity.com/paper/2106.06224/) (2021)
- [How Powerful are Decoder-Only Transformer Neural Models?](https://mlsanity.com/paper/2305.17026/) (2023)
- [Towards Efficient Auctions in an Auto-bidding World](https://mlsanity.com/paper/2103.13356/) (2021)
- [RTBAgent: A LLM-based Agent System for Real-Time Bidding](https://mlsanity.com/paper/2502.00792/) (2025)
- [AiAds: Automated and Intelligent Advertising System for Sponsored Search](https://mlsanity.com/paper/1907.12118/) (2019)
- [Trajectory-wise Iterative Reinforcement Learning Framework for Auto-bidding](https://mlsanity.com/paper/2402.15102/) (2024)
- [Offline Reinforcement Learning for Optimizing Production Bidding Policies](https://mlsanity.com/paper/2310.09426/) (2023)
- [Adversarial Constrained Bidding via Minimax Regret Optimization with Causality-Aware Reinforcement Learning](https://mlsanity.com/paper/2306.07106/) (2023)
- [Real-time Bidding Strategy in Display Advertising: An Empirical Analysis](https://mlsanity.com/paper/2212.02222/) (2022)
- [Multi-Platform Autobidding with and without Predictions](https://mlsanity.com/paper/2502.19317/) (2025)
- [Optimal Return-to-Go Guided Decision Transformer for Auto-Bidding in Advertisement](https://mlsanity.com/paper/2506.21956/) (2025)
- [Adscape: Harvesting and Analyzing Online Display Ads](https://mlsanity.com/paper/1407.0788/) (2014)

## Built on by (most cited first, 3 of 3)

- [When Deepfake Detection Meets Graph Neural Network:a Unified and Lightweight Learning Framework](https://mlsanity.com/paper/2508.05526/) (2025)
- [Beyond Single Slot: Joint Optimization for Multi-Slot Guaranteed Display Advertising](https://mlsanity.com/paper/2605.21556/) (2026)
- [AIGB-R1: Self-Evolving Generative Auto-Bidding via Hierarchical Planner-Executor Optimization](https://mlsanity.com/paper/2607.17281/) (2026)

## Abstract (arXiv)

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.