1 citations · 2 across the 13 of their papers we have counts for
16 papers
UniPolicy: Unified Objective-Specific Policies for Generative Search Advertising
Kun Yao, Yuhang Zhou, Yichi Zhang +6
Search advertising connects user intent with commercial content and plays a critical role in platform monetization. Recent systems typically align pretrained generative models with…
DRIVE: Distributional and Retrieval-Augmented Bidding with Value Evaluation
Miduo Cui, Haochen Wang, Shangqin Mao +6
Auto-bidding is a core component of real-time advertising systems, where decisions must optimize long-term performance under budget and cost constraints, while online exploration i…
HMAF: A Hierarchical Multi-Slot GD-RTB Allocation Framework
Tianxing Bu, Zhaoqi Zhang, Linyou Cai +7
In modern online advertising platforms, Guaranteed Delivery (GD) contracts coexist and bid with Real-Time Bidding (RTB) auctions. Recent approaches either decouple GD and RTB optim…
Beyond Single Slot: Joint Optimization for Multi-Slot Guaranteed Display Advertising
Zhaoqi Zhang, Jiaming Deng, Miao Xie +5
Guaranteed display advertising is crucial for platform monetization, yet existing methods often operate under a single-slot assumption, limiting their ability to optimize allocatio…
Understanding and Mitigating Spurious Signal Amplification in Test-Time Reinforcement Learning for Math Reasoning
Yongcan Yu, Lingxiao He, Jian Liang +5
Test-time reinforcement learning (TTRL) always adapts models at inference time via pseudo-labeling, leaving it vulnerable to spurious optimization signals from label noise. Through…
How to Train Your Deep Research Agent? Prompt, Reward, and Policy Optimization in Search-R1
Yinuo Xu, Shuo Lu, Jianjie Cheng +5
Deep Research agents tackle knowledge-intensive tasks through multi-round retrieval and decision-oriented generation. While reinforcement learning (RL) has been shown to improve pe…
Do MLLMs Really Understand Space? A Mathematical Reasoning Evaluation
Shuo Lu, Jianjie Cheng, Yinuo Xu +16
Multimodal large language models (MLLMs) have achieved strong performance on perception-oriented tasks, yet their ability to perform mathematical spatial reasoning, defined as the…
Reassessing the Role of Supervised Fine-Tuning: An Empirical Study in VLM Reasoning
Yongcan Yu, Lingxiao He, Shuo Lu +10
Recent advances in vision-language models (VLMs) reasoning have been largely attributed to the rise of reinforcement Learning (RL), which has shifted the community's focus away fro…
RIA: A Ranking-Infused Approach for Optimized listwise CTR Prediction
Guoxiao Zhang, Tan Qu, Ao Li +3
Reranking improves recommendation quality by modeling item interactions. However, existing methods often decouple ranking and reranking, leading to weak listwise evaluation models…
FITRep: Attention-Guided Item Representation via MLLMs
Guoxiao Zhang, Ao Li, Tan Qu +2
Online platforms usually suffer from user experience degradation due to near-duplicate items with similar visuals and text. While Multimodal Large Language Models (MLLMs) enable mu…
AI-Salesman: Towards Reliable Large Language Model Driven Telemarketing
Qingyu Zhang, Chunlei Xin, Xuanang Chen +7
Goal-driven persuasive dialogue, exemplified by applications like telemarketing, requires sophisticated multi-turn planning and strict factual faithfulness, which remains a signifi…
Generative Large-Scale Pre-trained Models for Automated Ad Bidding Optimization
Yu Lei, Jiayang Zhao, Yilei Zhao +4
Modern auto-bidding systems are required to balance overall performance with diverse advertiser goals and real-world constraints, reflecting the dynamic and evolving needs of the i…
Deterministic-Allocation and Anonymous Joint Advertising in E-commerce Platforms
Zhen Zhang, Luowen Liu, Wanzhi Zhang +5
With the advancement of machine learning, an increasing number of studies are employing automated mechanism design (AMD) methods for optimal auction design. However, all previous A…
Hierarchical Multi-agent Meta-Reinforcement Learning for Cross-channel Bidding
Shenghong He, Chao Yu
Real-time bidding (RTB) plays a pivotal role in online advertising ecosystems. Advertisers employ strategic bidding to optimize their advertising impact while adhering to various f…
Off-Policy Primal-Dual Safe Reinforcement Learning
Zifan Wu, Bo Tang, Qian Lin +5
Primal-dual safe RL methods commonly perform iterations between the primal update of the policy and the dual update of the Lagrange Multiplier. Such a training paradigm is highly s…
HiBid: A Cross-Channel Constrained Bidding System with Budget Allocation by Hierarchical Offline Deep Reinforcement Learning
Hao Wang, Bo Tang, Chi Harold Liu +7
Online display advertising platforms service numerous advertisers by providing real-time bidding (RTB) for the scale of billions of ad requests every day. The bidding strategy hand…