1 citations · 1 across the 14 of their papers we have counts for
17 papers
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…
Dr. Free: You Don't Need Difficulty Rewards for Self-Evolving Search Agents
Zhipeng Qian, Zihan Liang, Yufei Ma +10
A central limitation of current data-free self-evolution methods for training search agents is their reliance on difficulty-based proposer rewards. These methods reward a proposer…
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…
SRPO: Setwise Relative Policy Optimization for Multi-Agent Systems
Shengtian Yang, Ziyu Xiong, Yu Li +3
Multi-agent systems enable complex reasoning and tool use by coordinating agents that divide roles and refine candidate solutions. Existing methods typically update individual agen…
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…
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…
PlatformBid: An Auto-Bidding Benchmark from a Unified Advertising Platform's Perspective
Shengtian Yang, Yewen Li, Peng Jiang +4
Real-time bidding is central to computational advertising, comprising three elements: Supply Side Platform (SSP) selling ad impressions, Demand Side Platform (DSP) bidding for adve…
Reinforced Preference Optimization for Reasoning-Augmented Recommendations
Jingtong Gao, Zeyu Song, Chi Lu +7
Recommender systems are critical for delivering personalized content across digital platforms, and recent advances in Large Language Models (LLMs) offer new opportunities to enhanc…
Position-Aware Drafting for Inference Acceleration in LLM-Based Generative List-Wise Recommendation
Jiaju Chen, Chongming Gao, Chenxiao Fan +4
Large language model (LLM)-based generative list-wise recommendation has advanced rapidly, but decoding remains sequential and thus latency-prone. To accelerate inference without c…
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…
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…
TrackRec: Iterative Alternating Feedback with Chain-of-Thought via Preference Alignment for Recommendation
Yu Xia, Rui Zhong, Zeyu Song +5
The extensive world knowledge and powerful reasoning capabilities of large language models (LLMs) have attracted significant attention in recommendation systems (RS). Specifically,…
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.…
ACQ: A Deployed Two-Stage Framework for Automated Creative Quota Allocation in Large-Scale Online Advertising
Ruizhi Wang, Yu Rong, Kai Liu +4
In digital advertising, demand-side platforms (DSPs) allow advertisers to create multiple ad creatives from a single photo for real-time bidding. While increasing the number of cre…
LDACP: Long-Delayed Ad Conversions Prediction Model for Bidding Strategy
Peng Cui, Yiming Yang, Fusheng Jin +8
In online advertising, once an ad campaign is deployed, the automated bidding system dynamically adjusts the bidding strategy to optimize Cost Per Action (CPA) based on the number…