Bid optimization strategy in auto-bidding is central to computational advertising, achieving notable commercial success by optimizing advertisers' bids within constraints. Recently, generative models have revolutionized auto-bidding by directly learning a policy from large-scale datasets. Among them, the diffuser is su...
Ye-Wen Li, Jingtong Gao, Peng Jiang et al.· Proceedings of the 32nd ACM...· 0 citations
Auto-bidding is central to computational advertising, where strategies must maximize advertisers'conversion value under economic constraints. It has evolved from rule-based controllers to reinforcement learning and generative methods such as Decision Transformer (DT). Yet these methods increasingly mismatch the prevail...
Ye-Wen Li, Peng Jiang, Yi-Tian Li et al.· 0 citations
Multi-agent large language models solve complex tasks by coordinating several policies in a shared environment. However, existing reinforcement learning methods usually optimize each response or trajectory separately, even when several outputs jointly cause one state transition. Consequently, the update unit differs fr...
Sheng-Tian Yang, Zi-Yun Xiong, Yu Li et al.· 1 citation
AgentBrew is proposed, an offline training framework that learns effective tool-use policies from a single batch of raw interaction trajectories, without task verifiers or iterative on-policy rollouts, and demonstrates that fine-grained offline learning can recover useful supervision from raw trajectories that filterin...
Zhiyi Lyu, Ye-Wen Li, Longtao Zheng et al.· 2 citations
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