Multi-agent systems enable complex reasoning and tool use by coordinating agents that divide roles and refine candidate solutions. Existing methods typically update individual agent responses or treat a complete trajectory as one training example. However, these methods may produce misleading policy updates because the...
Sheng-Tian Yang, Zi-Yun Xiong, Yu Li et al.· 0 citations
This work proposes a Causal auto-Bidding method based on a Diffusion completer-aligner framework, termed CBD, which achieves superior performance on large-scale auto-bidding benchmarks, but also delivers significant improvements on an online advertising platform, including a 2.0% increase in target cost.
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
SRPO (Setwise Relative Policy Optimization), which treats the active set the minimal set of outputs consumed by one transition, as one multi-agent action, and unifies division of labor and joint co-evolution as actions with different set sizes.
Sheng-Tian Yang, Zi-Yun Xiong, Yu Li et al.· 4 citations
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, Long-Tao Zheng et al.· 2 citations
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