Zhaowei Zhang's Homepage (original) (raw)

Research Interests

• Generative AI
• Omni MLLM & World Models
• Efficient AI
• Reinforcement Learning

Zhaowei is pronounced as "Ju" (as in judge) + "ou" (as in out) + "Way"; Zhang, or Cheung in Hong Kong, is "Ju" (as in judge) + "on" | audio ([International Phonetic Alphabet, IPA]) here: [tʂɑuwei][tʂɑŋ].

I am currently a third-year Ph.D. candidate at Institute for AI, School of Intelligence Science and Technology, Peking University. Specifically, I am in the team of PAIR-Lab led by Prof. Yaodong Yang. The long-term goal of my research is to explore the edge of intelligence in the scaling process and how such intelligence can better serve human society. I have previously focused on problems in online convex learning, reinforcement learning, AI alignment, and LLM reasoning. Currently, the question I care most about is how to build a strong and efficient multimodal system that can make the most use of computational resources to achieve the greatest possible scaling. I welcome more friends to discuss these topics with me ☺️.

Selected Publications (* indicates equal contribution.)

2026

• Does LLM Alignment Really Need Diversity? An Empirical Study of Adapting RLVR Methods for Moral Reasoning
  Zhaowei Zhang, Xiaohan Liu, Xuekai Zhu, Junchao Huang, Ceyao Zhang, Zhiyuan Feng, Yaodong Yang, Xiaoyuan Yi, Xing Xie
  Preprint
  [Paper]

2025

• PoliCon: Evaluating LLMs on Achieving Diverse Political Consensus Objectives
  Zhaowei Zhang, Xiaobo Wang, Minghua Yi, Mengmeng Wang, Fengshuo Bai, Zilong Zheng, Yipeng Kang, Yaodong Yang
  ICLR 2026
  [Paper] [Website]
• Evaluating Generalization Capabilities of LLM-Based Agents in Mixed-Motive Scenarios Using Concordia
  NeurIPS DB Track 2025
  [Paper]
• Amulet: ReAlignment During Test Time for Personalized Preference Adaptation of LLMs
  Zhaowei Zhang, Fengshuo Bai, Qizhi Chen, Chengdong Ma, Mingzhi Wang, Haoran Sun, Zilong Zheng, Yaodong Yang
  ICLR 2025
  [Paper] [Website] [Code]

2024

• ValueDCG: Measuring Comprehensive Human Value Understanding Ability of Language Models
  Zhaowei Zhang, Fengshuo Bai, Jun Gao, Yaodong Yang
  NeurIPS 2025 Workshop on Regulatable ML
  [Paper] [Blog] [Chinese Blog]
• Foundational Challenges in Assuring Alignment and Safety of Large Language Models
  Core author
  TMLR
  [Paper] [Website]
• Roadmap on Incentive Compatibility for AI Alignment and Governance in Sociotechnical Systems
  Zhaowei Zhang, Fengshuo Bai, Mingzhi Wang, Haoyang Ye, Chengdong Ma, Yaodong Yang
  AGI 2025 (Oral)
  [Paper] [Chinese Blog]

2023

• AI Alignment: A Comprehensive Survey
  Core author
  ACM Computing Surveys
  [Paper] [Website]
• ProAgent: Building Proactive Cooperative AI with Large Language Models
  Ceyao Zhang, Kaijie Yang, Siyi Hu, Zihao Wang, Guanghe Li, Yihang Sun, Cheng Zhang,Zhaowei Zhang, Anji Liu, Song-Chun Zhu, Xiaojun Chang, Junge Zhang, Feng Yin, Yitao Liang, Yaodong Yang
  AAAI 2024 (Oral)
  [Paper]
• Heterogeneous Value Alignment Evaluation for Large Language Models
  Zhaowei Zhang, Nian Liu, Siyuan Qi, Ceyao Zhang, Ziqi Rong, Shuguang Cui, Song-Chun Zhu, Yaodong Yang
  AGI 2025 & AAAI 2024 Workshop: Public Sector LLMs (Oral)
  [Paper]
• STAS: Spatial-Temporal Return Decomposition for Solving Sparse Rewards Problems in Multi-agent Reinforcement Learning
  Sirui Chen *,Zhaowei Zhang *, Yali Du, Yaodong Yang
  AAAI 2024
  [Paper] [Code]

Blogs & Tutorials

• AI for Helping People to Find Consensus
  Abstract: This tutorial explains how to use LLMs as scalable "AI mediators" that infer preferences from large amounts of free-text opinions and generate consensus statements representing different groups, helping address information bandwidth limits, anchoring effects, and inefficiencies in real-world deliberation. It covers group-level generative social choice and stakeholder-level consensus generation and evaluation, and discusses challenges such as bias, dynamic negotiation, and interaction costs.
  [Tutorial Slides]
• The Three-Layer Paradigm for Implementing Sociotechnical AI Alignment: A Top-Down-Top Outlook
  Abstract: This blog clarifies what socio-technical systems (STS) mean in the context of AI alignment, resolving inconsistent definitions across scales. It presents a computable, multi-scale view of STS alignment problems and outlines possible research directions.
  [English Version] [Chinese Version]

Internship

Kling AI, Kuaishou Tech.
Research Intern, working with Xintao Wang
February 2026

Microsoft Research (MSRA)
Research Intern, working with Xing Xie & Xiaoyuan Yi
September 2025 - January 2026

Selected Awards

• Century Frontier Young Scholar Award, 2026.
• Huawei Spark Award, 2025. (the only student recipient) [News]

Services

• Reviewer for AI conferences represented by ICLR, NeurIPS, and ICML.
• Program Committee Member for AAAI 2026 AIA Track.
• Program Committee Member for AAAI 2026.
• Program Committee Member for DAI 2024.