2 citations · 4 across the 17 of their papers we have counts for
5 papers · 1 filter
Hierarchical Semantic RL: Tackling the Problem of Dynamic Action Space for RL-based Recommendations
Minmao Wang, Xingchen Liu, Shijie Yi +5
Recommender Systems (RS) are fundamental to modern online services. While most existing approaches optimize for short-term engagement, recent work has begun to explore reinforcemen…
Generative Auto-Bidding in Large-Scale Competitive Auctions via Diffusion Completer-Aligner
Yewen Li, Jingtong Gao, Nan Jiang +7
Auto-bidding is central to computational advertising, achieving notable commercial success by optimizing advertisers' bids within economic constraints. Recently, large generative m…
Navigate the Unknown: Enhancing LLM Reasoning with Intrinsic Motivation Guided Exploration
Jingtong Gao, Ling Pan, Yejing Wang +6
Reinforcement Learning (RL) has become a key approach for enhancing the reasoning capabilities of large language models. However, prevalent RL approaches like proximal policy optim…
Generative Auto-Bidding with Value-Guided Explorations
Jingtong Gao, Yewen Li, Shuai Mao +8
Auto-bidding, with its strong capability to optimize bidding decisions within dynamic and competitive online environments, has become a pivotal strategy for advertising platforms.…
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…