4 papers
SALT: When More Rollouts Don't Help in Group-Based Policy Optimization and How to Make Them Matter
Powei Chang, Jinpeng Zhang, Chaoqun Sun +6
Reinforcement learning with verifiable rewards (RLVR) often adopts GRPO-style group-relative updates, sampling multiple rollouts per prompt to construct normalized learning signals…
Constraint-Aware Generative Re-ranking for Multi-Objective Optimization in Advertising Feeds
Chenfei Li, Hantao Zhao, Weixi Yao +4
Optimizing reranking in advertising feeds is a constrained combinatorial problem, requiring simultaneous maximization of platform revenue and preservation of user experience. Recen…
Multi-Epoch learning with Data Augmentation for Deep Click-Through Rate Prediction
Zhongxiang Fan, Zhaocheng Liu, Jian Liang +5
This paper investigates the one-epoch overfitting phenomenon in Click-Through Rate (CTR) models, where performance notably declines at the start of the second epoch. Despite extens…
Adaptive Neural Ranking Framework: Toward Maximized Business Goal for Cascade Ranking Systems
Yunli Wang, Zhiqiang Wang, Jian Yang +4
Cascade ranking is widely used for large-scale top-k selection problems in online advertising and recommendation systems, and learning-to-rank is an important way to optimize the m…