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Enhancing Generative Auto-bidding with Offline Reward Evaluation and Policy Search

arXiv – CS AI|Zhiyu Mou, Yiqin Lv, Miao Xu, Qi Wang, Yixiu Mao, Jinghao Chen, Qichen Ye, Chao Li, Rongquan Bai, Chuan Yu, Jian Xu, Bo Zheng|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers developed AIGB-Pearl, a new AI-driven auto-bidding system that combines generative planning with policy optimization to improve advertising performance. The system addresses limitations of existing offline reinforcement learning methods by incorporating a trajectory evaluator and safe exploration mechanisms beyond static datasets.

Key Takeaways

→AIGB-Pearl integrates generative planning with policy optimization to enhance AI-generated bidding systems.
→The method uses a trajectory evaluator to assess generated bid scores and ensure safe exploration beyond offline data.
→A KL-Lipschitz-constrained score-maximization scheme enables efficient exploration while maintaining safety.
→Testing on both simulated and real-world advertising systems shows state-of-the-art performance.
→The approach addresses key limitations of existing offline reinforcement learning auto-bidding methods.

#ai-research #auto-bidding #reinforcement-learning #advertising #machine-learning #algorithmic-trading #optimization

Read Original →via arXiv – CS AI

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