Yiqun Chen
Ph.D. Candidate at GSAI, RUC.
Gaoling School of AI
Renmin University of China
Beijing, China
My name is Yiqun Chen (ιιΈηΎ€). Currently, I am pursuing my Ph.D. at the Gaoling School of Artificial Intelligence, Renmin University of China (RUC), under the guidance of Prof. Jiaxin Mao.
π¬ Research Interests
My research interests primarily lie in Multi-Agent Reinforcement Learning and Agentic Search:
- LLM Agent & Reinforcement Learning:
- General LLM-based Multi-Agent Optimization Framework (UnityMAS-O)
- Data Synthesis & Agent Memory & Evaluation/Reward
- Multi-Agent Reinforcement Learning (MARL)
- AI Search:
- Retrieval-Augmented Generation (RAG)
- Agentic Search & Deep Search/Research
- Information Retrieval (IR):
- Large Language Models for Ranking (LLM4Ranking)
- Application of Reinforcement Learning for IR (e.g., RL for Diversified Search)
π’ Industry Collaboration & Leadership
π Recent Focus: Multi-Agent/Agent-Swarm Joint Optimization (RL)
Recently, I have maintained close collaborations with leading tech companies on LLM-based Multi-Agent RL, leading the development of UnityMAS-O, a Ray + veRL-based multi-agent reinforcement learning framework that supports customizable agent workflows, flexible agent-to-model mapping, and scalable distributed PPO optimization across shared, partially shared, or independent models.
π Previous Internships My internship experiences include:
- XiaoHongShu (Dots Agent & AI Search) (β¨Ace Top Intern Program): End-to-end Multi-Agent RL optimization and full-link Agent research.
- Baidu (Search Dept. & Intelligent Cloud): Agentic Search, Dumate Agent research.
- ByteDance (Feishu/Lark): Memory-augmented AI search.
- Huawei (Noahβs Ark Decision Making & Reasoning Lab): Multi-Agent Reinforcement Learning (MARL).
- DiDi Chuxing (Ride-hailing Dept.): Pick-up/Drop-off location recommendation.
π¨βπ Job Market: Fall 2026 Internship
As a prospective Ph.D. graduate (Class of 2027), I am actively seeking a Fall 2026 Internship (targeting the 2027 campus recruitment season).
π€ Why me? My mission is to build robust, scalable Multi-Agent paradigms and efficient infra/training framework. I prioritize practical utility over theoretical narratives (rejecting mere βstorytellingβ). I am dedicated to bringing tangible performance gains and genuine, deployable innovation to industrial scenarios.
If you are looking for a researcher who focuses on what actually works, please contact me!
π Education
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Ph.D. Candidate in Artificial Intelligence Gaoling School of Artificial Intelligence (GSAI), Renmin University of China (RUC) 2023 - 2027 (Expected)
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M.Sc. in Pattern Recognition and Intelligent Systems Institute of Automation, Chinese Academy of Sciences (CASIA) 2020 - 2023
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B.Sc. in Automation Shandong University (SDU) 2016 - 2020
π° News
- 2026.5: π One paper is accepted by ICML 2026.
- 2025.12: π₯ We released a comprehensive survey: Deep Research: A Systematic Survey.
- 2025.9: ππ Two papers are accepted by NeurIPS 2025.
- 2025.8: π One paper is accepted by CIKM 2025.
- 2025.7: π One paper is accepted by MM 2025.
- 2025.6: π₯ Our AI Search Paradigm paper is publicly available.
- 2025.1: ππ Two first-author papers are accepted by WWW 2025.
- 2024.4: π One first-author paper is accepted by IJCAI 2024.
- 2023.9: I joined Renmin University of China to pursue my Ph.D.
- 2023.4: I joined the Search Department of Baidu Inc. as an algorithm intern.
πΊοΈ Visitors
selected publications
- IJCNN 2022
Commander-Soldiers Reinforcement Learning for Cooperative Multi-Agent SystemsIn International Joint Conference on Neural Networks (IJCNN), Dec 2022 - ICONIP 2022Multi-Agent Hyper-Attention Policy OptimizationIn International Conference on Neural Information Processing (ICONIP), Dec 2022