DRIVE: Distributional and Retrieval-Augmented Bidding with Value Evaluation
Miduo Cui · Haochen Wang · Shangqin Mao · Xun Yang · Qianlong Xie · Xingxing Wang · Xuri Ge · Ying Zhou · Zhiwei XU
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Miduo Cui · Haochen Wang · Shangqin Mao · Xun Yang · Qianlong Xie · Xingxing Wang · Xuri Ge · Ying Zhou · Zhiwei XU
Ziqi Zhao · Zhaochun Ren · Jiahong Zou · Liu Yang · Zhiwei XU · Xuri Ge · Zhumin Chen · xinyu ma · Daiting Shi · Shuaiqiang Wang · Dawei Yin · Xin Xin
Reinforcement learning with verifiable rewards (RLVR) has proven effective in enhancing the reasoning of large language models (LLMs). Monte Carlo Tree Search (MCTS)-based extensions improve upon vanilla RLVR (e.g., GRPO) by providing tree-based reasoning rollouts that enable fine-grained and seg...
Junchen Fu · Xuri Ge · Alexandros Karatzoglou · Ioannis Arapakis · Suzan Verberne · Joemon M. Jose · Zhaochun Ren
Generative recommendation provides a novel paradigm in which each item is represented by a discrete semantic ID (SID) learned from rich content. Most methods treat SIDs as predefined and train recommenders under static indexing. In practice, SIDs are optimized only for content reconstruction rath...
Fuhai Chen · Feng Zhang · Xiaoguang Ma · Yiyi Zhou · Jiarong Liu · Xuri Ge