arXiv:2509.15927v5 Announce Type: replace Abstract: Auto-bidding is a critical tool for advertisers to improve advertising performance. Recent progress has demonstrated that AI-Generated Bidding (AIGB), which learns a conditional generative planner from offline data, achieves superior performance compared to typical offline reinforcement learning (RL)-based auto-bidding methods. However, existing AIGB methods still face a performance bottleneck due to their inherent inability to explore beyond the static dataset with feedback. To address this, we propose \textbf{AIGB-Pearl} (\emph{\textbf{P}la
The paper addresses the current bottleneck of AI-generated bidding models, which are limited by static offline data, and proposes an advancement that integrates offline reward evaluation and policy search for improved performance.
This research introduces a method to overcome a significant limitation in AI-driven advertising, potentially leading to more efficient ad spending and higher returns for advertisers.
Existing AI-generated bidding systems, which previously struggled with exploring beyond static datasets, can now incorporate dynamic learning from offline reward evaluations and policy searches, making them more adaptable and effective.
- · Digital advertising platforms
- · Advertisers
- · E-commerce companies
- · Advertisers not adopting advanced AI bidding
Increased efficiency and effectiveness of auto-bidding algorithms in online advertising.
Greater competitive advantage for companies that integrate these advanced AI agents into their marketing strategies.
Potential for a more dynamic and personalized advertising landscape, as AI agents become more adept at real-time optimization.
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Read at arXiv cs.LG