GUIDE integrates a Decision Transformer for joint modeling of bidding actions and states with Q-value regularization for exploration and an IDM for safe policy fallback, outperforming baselines in simulations and real Taobao deployment with gains in GMV, clicks, cost, and ROI.
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SemBid injects LLM-encoded Task, History, and Strategy semantics as tokens into offline bidding trajectories and uses self-attention to outperform numerical-only baselines in performance, constraint satisfaction, and robustness.
PRL-PUTS casts utility-weight tuning as a one-step value-based RL task and uses scalarization-parameter Pareto sweeping at inference time to generate and govern a family of policies, reporting +0.13% lift in successful sessions on Pinterest Homefeed.
citing papers explorer
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Generative Auto-Bidding with Unified Modeling and Exploration
GUIDE integrates a Decision Transformer for joint modeling of bidding actions and states with Q-value regularization for exploration and an IDM for safe policy fallback, outperforming baselines in simulations and real Taobao deployment with gains in GMV, clicks, cost, and ROI.
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On the Role of Language Representations in Auto-Bidding: Findings and Implications
SemBid injects LLM-encoded Task, History, and Strategy semantics as tokens into offline bidding trajectories and uses self-attention to outperform numerical-only baselines in performance, constraint satisfaction, and robustness.
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A Production-Ready RL Framework for Personalized Utility Tuning with Pareto Sweeping in Pinterest Recommender Systems
PRL-PUTS casts utility-weight tuning as a one-step value-based RL task and uses scalarization-parameter Pareto sweeping at inference time to generate and govern a family of policies, reporting +0.13% lift in successful sessions on Pinterest Homefeed.