9 papers
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…
MindRec: A Diffusion-driven Coarse-to-Fine Paradigm for Generative Recommendation
Mengyao Gao, Chongming Gao, Haoyan Liu +5
Recent advancements in large language model-based recommendation systems often represent items as text or semantic IDs and generate recommendations in an auto-regressive manner. Ho…
GAS: Generative Auto-bidding with Post-training Search
Yewen Li, Shuai Mao, Jingtong Gao +6
Auto-bidding is essential in facilitating online advertising by automatically placing bids on behalf of advertisers. Generative auto-bidding, which generates bids based on an adjus…
AURO: Reinforcement Learning for Adaptive User Retention Optimization in Recommender Systems
Zhenghai Xue, Qingpeng Cai, Bin Yang +4
The field of Reinforcement Learning (RL) has garnered increasing attention for its ability of optimizing user retention in recommender systems. A primary obstacle in this optimizat…
From Principles to Applications: A Comprehensive Survey of Discrete Tokenizers in Generation, Comprehension, Recommendation, and Information Retrieval
Jian Jia, Jingtong Gao, Ben Xue +6
Discrete tokenizers have emerged as indispensable components in modern machine learning systems, particularly within the context of autoregressive modeling and large language model…
Value Function Decomposition in Markov Recommendation Process
Xiaobei Wang, Shuchang Liu, Qingpeng Cai +4
Recent advances in recommender systems have shown that user-system interaction essentially formulates long-term optimization problems, and online reinforcement learning can be adop…