Weinan Zhang
Professor
Works on reinforcement learning, agentic AI and physical AI, focusing on foundation models for physical agents.
Weinan Zhang's lab at SJTU works on reinforcement learning, generative & foundation models and large language models. 71 papers since 2023, 58 at top venues, mostly KDD and NeurIPS. Applications close .
At a glance
- papers, 2023–2026
- 19/ year
- at T1 venues
- 82%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 34 papers2025 · T1 28
- 17 papers2024 · T1 12
- 20 papers2023 · T1 18
T1 venuesOtherVenues: KDD, NeurIPS, ICLR, ICML, WWW
Research focuspaper titles that use each topic's words
- Large language models26 papers
- Reinforcement learning20 papers
- Generative & foundation models15 papers
- physical0 papers
- agentic0 papers
Paper list
From the lab homepage.
202534 papers
- SIGIR (T1)Action First: Leveraging Preference-Aware Actions for More Effective Decision-Making in Interactive Recommender Systems
- ECMLPKDDAdvKT: An Adversarial Multi-Step Training Framework for Knowledge Tracing
- NeurIPS (T1)AgentNet: Decentralized Evolutionary Coordination for LLM-based Multi-Agent Systems
- KDD (T1)An Automatic Graph Construction Framework based on Large Language Models for Recommendation
- Findings of ACLBoost, Disentangle, and Customize: A Robust System2-to-System1 Pipeline for Code Generation
- KDD (T1)Bursting Filter Bubble: Enhancing Serendipity Recommendations with Aligned Large Language Models
- Findings of ACLCodePRM: Execution Feedback-enhanced Process Reward Model for Code Generation
- ICLR (T1)ContraDiff: Planning Towards High Return States via Contrastive Learning
- ACL (T1)DebateCoder: Towards Collective Intelligence of LLMs via Test Case Driven LLM Debate for Code Generation
- KDD (T1)Diffusion Models for Recommender Systems: From Content Distribution To Content Creation
- SIGIR (T1)Efficiency Unleashed: Inference Acceleration for LLM-based Recommender Systems with Speculative Decoding
- TORSEfficient and Deployable Knowledge Infusion for Open-World Recommendations via Large Language Models
202417 papers
- ICML (T1)AlphaZero-Like Tree-Search can Guide Large Language Model Decoding and Training
- MIRAn Empirical Study on Google Research Football Multi-agent Scenarios
- TKDDAttacking Click-through Rate Predictors via Generating Realistic Fake Samples
- AAMAS (T1)Boosting Studies of Multi-Agent Reinforcement Learning on Google Research Football Environment: the Past, Present, and Future
- WWW (T1)ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Prediction
- ICML (T1)DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching
- WWW (T1)HiFI: Hierarchical Fairness-aware Integrated Ranking with Constrained Reinforcement Learning
- WWW (T1)InfoRank: Unbiased Learning-to-Rank via Conditional Mutual Information Minimization
- WSDM (T1)K2: A Foundation Language Model for Geoscience Knowledge Understanding and Utilization
- WWW (T1)M-scan: A Multi-Scenario Causal-driven Adaptive Network for Recommendation
- PAKDDMulti-sourced Integrated Ranking with Exposure Fairness
- ICLR (T1)ODICS: Revealing the Mystery of Distribution Correction Estimation via Orthogonal-gradient Update
202320 papers
- KDD (T1)A Feature-Based Coalition Game Framework with Privileged Knowledge Transfer for User-tag Profile Modeling
- SCISA survey on model-based reinforcement learning
- JMLR (T1)Adaptation Augmented Model-based Policy Optimization
- KDD (T1)Building Self-Adaptive Recommendation Models via Responsive Error Compensation Loop
- KDD (T1)Deep Landscape Forecasting in Multi-Slot Real-Time Bidding
- KDD (T1)Dense Representation Learning and Retrieval for Tabular Data Prediction
- ICML (T1)GEAR: A GPU-Centric Experience Replay System for Large Reinforcement Learning Models
- KDD (T1)GMOCAT: A Graph-Enhanced Multi-Objective Method for Computerized Adaptive Testing
- IJCAI (T1)Large Decision Models
- AAAI (T1)Learning Decomposed Spatial Relations for Multi-Variate Time-Series Modeling
- KDD (T1)Learning Multi-Agent Intention-Aware Communication for Optimal Multi-Order Execution in Finance
- TOISLearning to Retrieve User Behaviors for Click-Through Rate Estimation
Where alumni went
Alumni are not listed on the lab homepage.
Apply to SJTUFall 2027
Computer Science PhD (international applicants)
- Round
- Self-funded
- Opens
- —
- GRE
- See program page
- English
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- Fee
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Not yet confirmed for this intake.
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