2 citations · 4 across the 17 of their papers we have counts for
10 papers · 1 filter
SCA: Spatial Credit Assignment for Reinforcement Learning of GUI Agents
Shengtian Yang, Ziyu Xiong, Kaibing Yang +6
GUI agents automate tasks on digital devices by grounding language instructions in visual interfaces. Existing group-relative reinforcement learning improves GUI action prediction…
OneBid: A Unified Auto-Bidding Foundation Model for Diverse oCPX Advertising Scenarios
Yewen Li, Peng Jiang, Yitian Li +3
Auto-bidding is central to computational advertising, where strategies must maximize advertisers' conversion value under economic constraints. It has evolved from rule-based contro…
AgentBrew: Offline Tool-Use Agent Learning from Raw Real-World Trajectories
Zhiyi Lyu, Yewen Li, Longtao Zheng +7
LLM-based agents are increasingly deployed in real-world applications through tool-use APIs, yet training them for specific environments remains fundamentally difficult: real-world…
HubMixer: Progressive Latent Hub Mixing for Parameter-Efficient Feature Interaction in Recommendation
Jie Zhou, Zixian Gong, Wenhao Li +6
Learning effective feature interactions is central to industrial recommendation and advertising ranking systems. Recent token-mixing architectures simplify self-attention with ligh…
Hierarchical Residual Policy Optimization for Generative Recommendations
Kaifeng Guo, Yiming Yang, Jingtong Gao +6
Generative recommenders select items by autoregressively decoding semantic identifiers (SIDs), whose token positions induce a coarse-to-fine hierarchy over the item space. In pract…
From Understanding to Action: Feedback-Grounded Policy Discovery for Generative Recommendation
Zhi Chen, Minmao Wang, Xingchen Liu +8
Semantic-ID-based generative recommenders enable efficient next-item generation, but their item-level supervision mainly captures behavioral co-occurrence and local transitions. La…
LBM: Hierarchical Large Auto-Bidding Model via Reasoning and Acting
Yewen Li, Zhiyi Lyu, Peng Jiang +3
The growing scale of ad auctions on online advertising platforms has intensified competition, making manual bidding impractical and necessitating auto-bidding to help advertisers a…
Phase-Aware Mixture of Experts for Agentic Reinforcement Learning
Shengtian Yang, Yu Li, Shuo He +4
Reinforcement learning (RL) has equipped LLM agents with a strong ability to solve complex tasks. However, existing RL methods normally use a \emph{single} policy network, causing…
Jointly Optimizing Debiased CTR and Uplift for Coupons Marketing: A Unified Causal Framework
Siyun Yang, Shixiao Yang, Jian Wang +6
In online advertising, marketing interventions such as coupons introduce significant confounding bias into Click-Through Rate (CTR) prediction. Observed clicks reflect a mixture of…
C2:Cross learning module enhanced decision transformer with Constraint-aware loss for auto-bidding
Jinren Ding, Xuejian Xu, Shen Jiang +3
Decision Transformer (DT) shows promise for generative auto-bidding by capturing temporal dependencies, but suffers from two critical limitations: insufficient cross-correlation mo…