Model-Based Reinforcement Learning Auto‑Bidding Algorithms for Online Advertising

The paper introduces a model‑based reinforcement‑learning auto‑bidding framework that learns a neural‑network environment model from real logs, generates confidence‑aware virtual data fused with real data, and employs the COMBO+MICRO stabilizer and a Lagrange‑dual method for ROI‑constrained bidding, delivering up to 6.8 % higher consumption, 5 % GMV growth and 3.7 % ROI improvement on Alibaba’s platform.

Reinforcement Learningauto-biddingbudget constrained bidding

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Model-Based Reinforcement Learning Auto‑Bidding Algorithms for Online Advertising

Alimama Tech

Dec 4, 2024 · Artificial Intelligence

AIGB: Generative Auto‑Bidding via Diffusion Modeling

AIGB, introduced by Alibaba Mama in 2023, reframes large‑scale ad‑auction auto‑bidding as a generative sequence task using diffusion models, achieving up to 5 % GMV gains, improved stability and interpretability, and is now commercialized, open‑sourced, and featured in a NeurIPS‑endorsed competition.

AIGenerative ModelsReinforcement Learning

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AIGB: Generative Auto‑Bidding via Diffusion Modeling

Alimama Tech

Jul 29, 2024 · Artificial Intelligence

Generative Auto-bidding via Diffusion Modeling (AIGB)

The paper presents AIGB, a generative auto‑bidding framework that replaces reinforcement‑learning with a conditional diffusion model to generate optimal bidding trajectories, and demonstrates through offline benchmarks and Alibaba’s online A/B tests that it consistently outperforms RL baselines, boosting buy count, GMV, and ROI while maintaining low latency.

Generative ModelsMarketing AIReinforcement Learning

0 likes · 18 min read

Generative Auto-bidding via Diffusion Modeling (AIGB)

Alimama Tech

Nov 28, 2023 · Artificial Intelligence

Evolution of Alibaba's AI-Driven Advertising Decision Technologies

The article traces Alibaba’s Alimama platform from classic control‑based bidding through linear programming and reinforcement‑learning approaches to generative‑AI‑driven strategies, detailing how deep‑learning models, offline and sustainable online RL frameworks, and large‑language‑model‑based bidding reshape automated auctions, fairness, and scalability in e‑commerce advertising.

AIAuction DesignReinforcement Learning

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Evolution of Alibaba's AI-Driven Advertising Decision Technologies

Alimama Tech

Apr 12, 2023 · Artificial Intelligence

Truthful Auction Mechanisms for Mixed Utility and Value Maximizers in Online Advertising

The paper introduces two truthful auction mechanisms—MPU for public‑type and MPR for private‑type bidders—that combine VCG payments for utility‑maximizers with GSP payments for value‑maximizers, achieving incentive compatibility, individual rationality, robustness, and a social‑welfare approximation of up to 2 (optimal within a 1.25 factor) in mixed online advertising markets.

GSPVCGauction theory

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Truthful Auction Mechanisms for Mixed Utility and Value Maximizers in Online Advertising

Alimama Tech

Dec 28, 2022 · Artificial Intelligence

Sustainable Online Reinforcement Learning for Auto-bidding (SORL)

The Sustainable Online Reinforcement Learning (SORL) framework tackles offline inconsistency in auto‑bidding by iteratively gathering safe online data from real ad systems with a Lipschitz‑based exploration method and training a variance‑suppressed conservative Q‑learning policy, achieving safer, more stable, and higher‑performing bids on Alibaba’s platform.

Reinforcement Learningauto-biddingoffline inconsistency

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Sustainable Online Reinforcement Learning for Auto-bidding (SORL)