Jiangjie Chen

Jiangjie Chen

Researcher

ByteDance Seed

Biography

Jiangjie Chen (陈江捷) is a researcher at ByteDance Seed Team. In 2024, he earned his Ph.D. at Fudan University in the School of Computer Science, Shanghai, China. His current interested research topics are mostly around building reasoning models and autonomous agents:

  1. Reasoning Models: Advancing research on incentivizing and understanding advanced reasoning and planning capabilities from large models.
  2. Autonomous Agents: Developing advanced methods for autonomous, trustworthy, and personalized agents. This extends towards the exploration of their interactions with multiple agents and real environments.
Interests
  • Large Language Models
  • Reasoning
  • Mountaineering 🧗‍♂️
  • Tennis 🎾
  • Musicals
Education

  • Ph.D. in CS, 2019 - 2024

    Fudan University

  • B.S. in CS (honors), 2014 - 2019

    Fudan University

News

  • May. 2025: Check out Enigmata! We propose a comprehensive suite of puzzles for improving logical reasoning of reasoning models, tailored for RLVR training. We find that not only do such puzzles drastically improve puzzle reasoning of LLMs, but also improve SoTA models such as Seed1.5-Thinking on challenging reasoning tasks such as AIME and GPQA! This is a free lunch for SoTA models, since Enigmata is synthetic and can be generated at scale!

  • May. 2025: I was awarded with Nomination Award for Outstanding Doctoral Dissertation of Shanghai Computer Society!

  • May. 2025: Our papers DEEPER and HistoryAnalogy are accepted to ACL 2025!

  • May. 2025: Our paper CoSER is accepted to ICML 2025! Check out this comprehensive resource for role-playing agents!

  • Apr. 2025: Presenting Seed-Thinking-v1.5 from ByteDance Seed Team, a cutting-edge reasoning model that’s incredible in math, code, science, and logical reasoning!

  • Mar. 2025: DAPO is out! A new critic-free RL algorithm that directly trains a pre-trained base model to SoTA performance on AIME 2024 without any SFT.

  • Mar. 2025: Four papers accepted to NAACL 2025: SelfGoal, EvoAgent, EasyTool and Barrier in Language Agent Planning.

  • Oct. 2024: Three papers accepted to NeurIPS 2024 Workshop on Open-World Agents: EvoAgent, SelfGoal and AucArena. See you in Vancouver!

  • Sep. 2024: Our survey paper on role-playing agents is accepted to TMLR!

  • Sep. 2024: We have three accepted papers in EMNLP 2024! Two main papers are Segment+ on long-context processing with short-context models, and CROSS on role-playing evaluation, and one finding paper DetectBench on benchmarking detective reasoning.

  • Jul. 2024: Our work on Irrelevant Evidence got accepted in COLM 2024!

  • Jul. 2024: I have graduated from Fudan University, and will officially join ByteDance Seed Team as a Full-time researcher.

  • Jun. 2024: How to automatically extend the specialized agent to multi-agent systems to improve task-solving capability? We propose EvoAgent, a generic method to automatically extend expert agents to multi-agent systems via the evolutionary algorithm. EvoAgent can be generalized to any LLM-based agent framework, and significantly enhance the task-solving capabilities of LLM-based agents!

  • Jun. 2024: Want your agents to win an auction for you? But does your agent know what it means by such a vague and high-level goal as “winning an auction”? Check out SelfGoal! We propose an automatic approach that enhances language agents’ capabilities to achieve high-level goals with limited instructions and delayed feedback by adaptively breaking down goals into practical subgoals. Really excited about automating agents to do high-level task with minimal human instructions!

  • Jun. 2024: TravelPlanner got a Spotlight recommendation at ICML 2024!

  • May 2024: Just defended my thesis, officially a Dr. :)

  • May 2024: Four papers are accepted to the main conference of ACL 2024! They are: TimeArena, AnalogyKB, InCharacter and GumbelSoft! See you in Bangkok :)

  • May 2024: Our TravelPlanner got accepted to ICML 2024!

Experience

 
 
 
 
 
ByteDance
Researcher
ByteDance
Jul 2024 – Present Shanghai, China
 
 
 
 
 
Allen Institute for AI
Research Intern
Allen Institute for AI
Jun 2023 – Sep 2023 Seattle, Washington, U.S.
Aristo Team, mentored by Dr. Kyle Richardson. Responsibilities: Work on multi-agent reasoning and planning with large language models.
 
 
 
 
 
UC Santa Barbara
Visiting Research Intern
UC Santa Barbara
Sep 2021 – May 2023 Remote
Hosted by Prof. Lei Li. Responsibilities: Work on machine reasoning over language with large language models.
 
 
 
 
 
ByteDance AI Lab
Research Intern
ByteDance AI Lab
Nov 2019 – May 2023 Shanghai, China
Mentored by Prof. Lei Li, Prof. Hao Zhou, and Dr. Changzhi Sun. Work on Knowledge-guided text generation and natural language reasoning.

Awards

Nomination Award for Outstanding Doctoral Dissertation of Shanghai Computer Society
Shanghai Computer Society May 2025
Excellent Graduates of Shanghai
Fudan University Apr 2024
ACL 2023 Outstanding Paper Award
Association for Computational Linguistics Jul 2023
China National Scholarship for Doctoral Students
Fudan University Oct 2022
Honor Student Award in Computer Science of Top Talent Undergraduate Training Program
Fudan University Jun 2019

Featured Publications

Jiangjie Chen, Qianyu He, Siyu Yuan, Aili Chen, Zhicheng Cai, Weinan Dai, Hongli Yu, Qiying Yu, Xuefeng Li, Jiaze Chen, Hao Zhou, Mingxuan Wang
May, 2025 Technical Report
Enigmata: Scaling Logical Reasoning in Large Language Models with Synthetic Verifiable Puzzles
Enigmata: Scaling Logical Reasoning in Large Language Models with Synthetic Verifiable Puzzles

We introduce Enigmata, the first comprehensive suite tailored for improving LLMs with puzzle reasoning skills.
PDF Cite Dataset Project Code Model
ByteDance Seed
April, 2025 Technical Report
Seed-Thinking-v1.5: Advancing Superb Reasoning Models with Reinforcement Learning
Seed-Thinking-v1.5: Advancing Superb Reasoning Models with Reinforcement Learning

We introduce Seed-Thinking-v1.5, a Mixture-of-Experts (MoE) model with a relatively small size, featuring 20B activated and 200B total parameters, capable of reasoning through thinking before responding, resulting in improved performance on a widerange of benchmarks.
PDF Project
Qiying Yu, Zheng Zhang, Ruofei Zhu, Yufeng Yuan, Xiaochen Zuo, Yu Yue, Tiantian Fan, Gaohong Liu, Lingjun Liu, Xin Liu, Haibin Lin, Zhiqi Lin, Bole Ma, Guangming Sheng, Yuxuan Tong, Chi Zhang, Mofan Zhang, Wang Zhang, Hang Zhu, Jinhua Zhu, Jiaze Chen, Jiangjie Chen, Chengyi Wang, Hongli Yu, Weinan Dai, Yuxuan Song, Xiangpeng Wei, Hao Zhou, Jingjing Liu, Wei-Ying Ma, Ya-Qin Zhang, Lin Yan, Mu Qiao, Yonghui Wu, Mingxuan Wang
March, 2025 Preprint
DAPO: An Open-source LLM Reinforcement Learning System At Scale
DAPO: An Open-source LLM Reinforcement Learning System At Scale

We introduce DAPO, a Decoupled Clip and Dynamic sAmpling Policy Optimization algorithm, and fully open-source a state-of-the-art large-scale RL system that achieves 50 points on AIME 2024 using Qwen2.5-32B base model.
PDF Cite Dataset Project Code Model
Aili Chen, Xuyang Ge, Ziquan Fu, Yanghua Xiao, Jiangjie Chen
September, 2024 Preprint
TravelAgent: An AI Assistant for Personalized Travel Planning
TravelAgent: An AI Assistant for Personalized Travel Planning

We introduce TravelAgent, an LLM-powered travel planning system that generates rational, comprehensive, and personalized itineraries through four modules, demonstrating effectiveness in dynamic scenarios.
PDF Cite Demo
Jian Xie, Kai Zhang, Jiangjie Chen, Tinghui Zhu, Renze Lou, Yuandong Tian, Yanghua Xiao, Yu Su
February, 2024 In The Forty-first International Conference on Machine Learning (ICML 2024), Spotlight
TravelPlanner: A Benchmark for Real-World Planning with Language Agents
TravelPlanner: A Benchmark for Real-World Planning with Language Agents

We introduced TravelPlanner, a benchmark for assessing language agents’ planning abilities, showing that even advanced models like GPT-4 face difficulties with complex tasks.
PDF Cite Dataset Code Demo
Jian Xie, Kai Zhang, Jiangjie Chen, Renze Lou, Yu Su
May, 2023 In The Twelfth International Conference on Learning Representations (ICLR 2024), Spotlight
Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflicts
Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflicts

We present the first comprehensive and controlled investigation into the behavior of large language models when encountering knowledge conflicts.
PDF Cite Code
Siyu Yuan, Jiangjie Chen, Ziquan Fu, Xuyang Ge, Soham Shah, Charles Robert Jankowski, Yanghua Xiao, Deqing Yang
May, 2023 In The 61th Annual Meeting of the Association for Computational Linguistics (ACL 2023), Outstanding Paper Award
Distilling Script Knowledge from Large Language Models for Constrained Language Planning
Distilling Script Knowledge from Large Language Models for Constrained Language Planning

We propose an over-generate-then-filter approach to improve large language models (LLMs) on constrained language planning, and use it to distill a novel constrained language planning dataset, CoScript.
PDF Cite Poster Slides Code

Recent Publications

Quickly discover relevant content by filtering publications.
Jiangjie Chen, Qianyu He, Siyu Yuan, Aili Chen, Zhicheng Cai, Weinan Dai, Hongli Yu, Qiying Yu, Xuefeng Li, Jiaze Chen, Hao Zhou, Mingxuan Wang (2025). Enigmata: Scaling Logical Reasoning in Large Language Models with Synthetic Verifiable Puzzles. Technical Report.

PDF Cite Dataset Project Code Model

ByteDance Seed (2025). Seed-Thinking-v1.5: Advancing Superb Reasoning Models with Reinforcement Learning. Technical Report.

PDF Project

Qiying Yu, Zheng Zhang, Ruofei Zhu, Yufeng Yuan, Xiaochen Zuo, Yu Yue, Tiantian Fan, Gaohong Liu, Lingjun Liu, Xin Liu, Haibin Lin, Zhiqi Lin, Bole Ma, Guangming Sheng, Yuxuan Tong, Chi Zhang, Mofan Zhang, Wang Zhang, Hang Zhu, Jinhua Zhu, Jiaze Chen, Jiangjie Chen, Chengyi Wang, Hongli Yu, Weinan Dai, Yuxuan Song, Xiangpeng Wei, Hao Zhou, Jingjing Liu, Wei-Ying Ma, Ya-Qin Zhang, Lin Yan, Mu Qiao, Yonghui Wu, Mingxuan Wang (2025). DAPO: An Open-source LLM Reinforcement Learning System At Scale. Preprint.

PDF Cite Dataset Project Code Model

Lida Chen, Dong Xu, Chenxin An, Xintao Wang, Yikai Zhang, Jiangjie Chen, Zujie Liang, Feng Wei, Jiaqing Liang, Yanghua Xiao, Wei Wang (2025). PowerAttention: Exponentially Scaling of Receptive Fields for Effective Sparse Attention. Preprint.

PDF Cite Code

Aili Chen, Chengyu Du, Jiangjie Chen, Jinghan Xu, Yikai Zhang, Siyu Yuan, Zulong Chen, Liangyue Li, Yanghua Xiao (2025). DEEPER Insight into Your User: Directed Persona Refinement for Dynamic Persona Modeling. The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025).

PDF Cite Code

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Popular Topics

Agent Analogical Reasoning Benchmark Constrained Generation Dataset Diversified Generation Explainable NLP Faithfulness and Factuality Information Extraction Knowledge Graph Large Language Models Large Reasoning Models LLM Analysis Planning Reasoning Reinforcement Learning Retrieval Role-Playing Agent Simulated Environment Text Generation

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