Tag
Pinterest researchers present RLBP, a hybrid reinforcement-learning architecture deployed in production that uses an RL meta-controller to select daily campaign-specific PID parameters for auto-bidding, achieving statistically significant eCPC reductions without revenue degradation in online A/B tests and post-rollout holdout measurements.
OneBid is a unified auto-bidding foundation model that addresses challenges in diverse oCPX advertising scenarios by using Mixture-of-Experts architecture and CROP optimization, with deployment at Kuaishou showing significant improvements like +13.1% in ROAS.
This paper introduces PlatformBid, the first comprehensive auto-bidding benchmark designed from a unified advertising platform perspective, along with BidFlow, a novel flow-matching-based auto-bidding method. Experiments show BidFlow improves target cost by +0.68% in online tests on Kuaishou.
This paper introduces DRIVE, a unified Transformer-based framework for offline auto-bidding that decouples candidate action generation from decision making, combining distributional action modeling, retrieval-augmented candidate generation, and value-based evaluation to improve bidding performance under budget and cost constraints.
This paper introduces Guide, a framework that combines a Decision Transformer with Q-value guidance and an inverse dynamics module to balance exploration and safety in automated bidding for digital advertising, demonstrating effectiveness on public datasets and simulated auctions.