arXiv:2606.14192v1 Announce Type: new Abstract: Auto-bidding is a core component of real-time advertising systems, where decisions must optimize long-term performance under budget and cost constraints, while online exploration is prohibitively risky. Offline reinforcement learning and, more recently, Transformer-based sequence modeling have shown promise for learning bidding policies from logged data, but their unimodal and purely parametric formulations often collapse multiple effective bidding strategies into suboptimal averaged actions and perform unreliably under sparse or long-tail traffi
The continuous evolution of AI in advertising demands more sophisticated bidding mechanisms, pushing the boundaries beyond traditional RL or Transformer models.
Improved auto-bidding directly impacts the efficiency and profitability of digital advertising, a multi-trillion dollar industry, by optimizing spending and targeting.
This research introduces a novel approach that combines distributional and retrieval-augmented methods to overcome limitations of existing auto-bidding systems, especially in sparse traffic.
- · Ad platforms
- · Advertisers
- · Adtech companies
- · AI researchers
- · Companies relying on suboptimal bidding strategies
- · Legacy ad optimization firms
More precise ad spending leads to higher ROI for advertisers.
Increased competition among ad platforms to adopt similar advanced AI approaches.
The development of 'AI agents' specialized in optimizing complex financial or resource allocation tasks based on similar principles.
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Read at arXiv cs.LG