HALO: Hindsight-Augmented Learning for Online Auto-Bidding

2025-08-05

AI Summary

Multi-constraint bidding (MCB) in digital advertising faces two key challenges: low sample efficiency and poor generalization across unseen budget–ROI configurations. To address these, we propose an end-to-end differentiable online learning framework. First, we design a theoretically grounded retrospective mechanism that repurposes historical exploration trajectories as training signals for arbitrary constraint combinations, significantly improving sample reuse. Second, we model the bidding policy using B-spline functions, enabling a continuous, gradient-aware mapping over the constraint space and explicitly encoding the physical relationship between constraints and bid coefficients. Evaluated on large-scale industrial datasets, our method substantially reduces constraint violation rates and increases gross merchandise value (GMV). Crucially, it demonstrates strong zero-shot adaptability to previously unseen budget and ROI configurations—without retraining—thereby enhancing operational robustness and scalability in real-world auction systems.

Technology Category

• Machine Learning: Online Learning & Bandits
• Search and Optimization: Learning to Search
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising

Abstract

Digital advertising platforms operate millisecond-level auctions through Real-Time Bidding (RTB) systems, where advertisers compete for ad impressions through algorithmic bids. This dynamic mechanism enables precise audience targeting but introduces profound operational complexity due to advertiser heterogeneity: budgets and ROI targets span orders of magnitude across advertisers, from individual merchants to multinational brands. This diversity creates a demanding adaptation landscape for Multi-Constraint Bidding (MCB). Traditional auto-bidding solutions fail in this environment due to two critical flaws: 1) severe sample inefficiency, where failed explorations under specific constraints yield no transferable knowledge for new budget-ROI combinations, and 2) limited generalization under constraint shifts, as they ignore physical relationships between constraints and bidding coefficients. To address this, we propose HALO: Hindsight-Augmented Learning for Online Auto-Bidding. HALO introduces a theoretically grounded hindsight mechanism that repurposes all explorations into training data for arbitrary constraint configuration via trajectory reorientation. Further, it employs B-spline functional representation, enabling continuous, derivative-aware bid mapping across constraint spaces. HALO ensures robust adaptation even when budget/ROI requirements differ drastically from training scenarios. Industrial dataset evaluations demonstrate the superiority of HALO in handling multi-scale constraints, reducing constraint violations while improving GMV.

Problem

• Improves auto-bidding adaptation for diverse advertiser budgets and ROI targets
• Addresses sample inefficiency and poor generalization in traditional auto-bidding
• Enables robust bidding under drastic budget/ROI shifts via hindsight learning

Innovation

• Hindsight-augmented learning repurposes explorations
• B-spline enables continuous bid mapping
• Trajectory reorientation for arbitrary constraints