Ling Yang β Princeton University
Postdoctoral Scholar Β· Princeton University
I am currently a Postdoctoral Scholar in the Department of ECE at Princeton University, co-affiliated with Princeton AI Lab, fortunately working with Prof. Mengdi Wang. Prior to this, I served as a Senior Research Assistant at Princeton University from January to July 2025. I received my Ph.D. degree from Peking University in July 2025, jointly supervised by Prof. Bin Cui and Prof. Luxia Zhang. During my doctoral studies, I was selected for the ByteDance Top Seed Talent Program.
I was also fortunate to collaborate with Yang Song, Shuicheng Yan, Ming-Hsuan Yang, Bernard Ghanem, Jiajun Wu, Stefano Ermon, Jure Leskovec, Yejin Choi, and James Zou.
My research builds toward Super Generative Intelligence β see Research Summary and Blog for a full overview of ongoing work across generative model foundations (large language models, reinforcement learning, and diffusion models), multimodal applications, and intelligent agent systems. I lead the Gen-Verse team at Princeton. Our open-source projects have accumulated 10,000+ GitHub Stars and 200,000+ HuggingFace Downloads.
Open Positions
We are looking for collaborators for research in LLM/MLLM Post-Training, Diffusion LLMs, World Modeling, and Agent Training. Positions are available across multiple levels and institutions:
Intern Research
Master's Student
PhD Student
Postdoc Researcher
π Peking University π Princeton University π Stanford University
We take collaboration seriously β please make sure the following apply before reaching out:
- 01Self-motivated β you drive your own execution and don't need to be pushed to make progress.
- 02Full commitment of 3+ months β you can dedicate yourself entirely to the project without major competing obligations.
- 03Fast execution β we define the research direction; you move quickly, iterate efficiently, and deliver results.
In return, we offer:
- β¦Frontier research perspective β direct exposure to the latest ideas and ongoing work at the cutting edge of generative AI.
- β¦Substantial compute resources β access to the compute you need to run large-scale experiments without bottlenecks.
- β¦Strong connections β academic network spanning Stanford, Princeton, MIT, PKU, Tsinghua and other top institutions, plus industry partners including Google DeepMind, ByteDance, Apple, Microsoft, and Meta.
The central goal is to build a unified system for Super Generative Intelligence β advancing AI toward active reasoning, simulation, and discovery in the physical world. I currently pursue the core algorithmic and architectural advances that power the next generation of AI β spanning language model reasoning, large-scale reinforcement learning, intelligent agent systems, and diffusion model innovations.
Generative Model Foundations
Generative Applications
Multimodal Content Generation
IterComp, VideoTetris, SGDiff, ScoreLiDAR, IPDreamer, EditWorld, Trans4D, WideRange4D, OmniVerifier, MMaDA-Parallel, UltraViCo
π Book Publication
"Diffusion Model: Theory, Application, and Code Practice of Generative AI Models"
Published by Electronics Industry Press (η΅εε·₯δΈεΊηη€Ύ), 2023 Β· Purchase Link Β· Selected as Annual Outstanding Author
We release OpenClaw-RL, an open-source framework for personalizing agents through conversational fine-tuning.
2 papers about multimodal reinforcement learning are accepted by CVPR 2026.
3 papers about LLMs and Agents are accepted by EMNLP 2025, including EmoAgent Oral, Top 1% and TreeBoN.
Invited to participate in a roundtable forum at WAIC 2025, hosted by Prof. Dahua Lin.
I was invited as an Area Chair at NeurIPS 2025.
Invited to give a talk at Princeton AI Lab, hosted by Prof. Mengdi Wang.
5 papers about Diffusion Models and LLMs are accepted by NeurIPS 2024, including Buffer of Thoughts Spotlight, Top 3%.
*Co-first author, +Corresponding author. For a complete list, see my Google Scholar profile.
Recent Highlighted Work
Technical Report
OpenClaw-RL: Personalize Your Agents with Conversational Fine-Tuning
Preprint
RLAnything: Forge Environment, Policy, and Reward Model in Completely Dynamic RL System
Yinjie Wang, Tianbao Xie, Ke Shen, Mengdi Wang, Ling Yang+
Preprint
Latent Collaboration in Multi-Agent Systems
Jiaru Zou, Xiyuan Yang, Ruizhong Qiu, Gaotang Li, Katherine Tieu, Pan Lu, Ke Shen, Hanghang Tong, Yejin Choi, Jingrui He, James Zou, Mengdi Wang, Ling Yang+
NeurIPS 2024Spotlight Top 3%
Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models
Ling Yang+, Zhaochen Yu, Tianjun Zhang, Shiyi Cao, Minkai Xu, Wentao Zhang, Joseph E Gonzalez, Bin Cui
ICML 2024
Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs
Ling Yang+, Zhaochen Yu, Chenlin Meng, Minkai Xu, Stefano Ermon, Bin Cui
NeurIPS 2025
MMaDA: Multimodal Large Diffusion Language Models
Ling Yang+, Ye Tian, Bowen Li, Xinchen Zhang, Ke Shen, Yunhai Tong, Mengdi Wang
ICLR 2026
Revolutionizing Reinforcement Learning Framework for Diffusion Large Language Models
Yinjie Wang, Ling Yang*+, Bowen Li, Ye Tian, Ke Shen, Mengdi Wang
Preprint
ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates
Ling Yang+, Zhaochen Yu, Bin Cui, Mengdi Wang
NeurIPS 2025Spotlight Top 3%
Co-Evolving LLM Coder and Unit Tester via Reinforcement Learning (ReasonFlux-Coder)
Yinjie Wang, Ling Yang*+, Ye Tian, Ke Shen, Mengdi Wang
NeurIPS 2025
ReasonFlux-PRM: Trajectory-Aware PRMs for Long Chain-of-Thought Reasoning in LLMs
Jiaru Zou, Ling Yang*+, Jingwen Gu, Jiahao Qiu, Ke Shen, Jingrui He, Mengdi Wang
ICLR 2025
SuperCorrect: Advancing Small LLM Reasoning with Thought Template Distillation and Self-Correction
Ling Yang+, Zhaochen Yu, Tianjun Zhang, Minkai Xu, Joseph E. Gonzalez, Bin Cui, Shuicheng Yan
NeurIPS 2025Spotlight Top 3%
Transformer Copilot: Learning from The Mistake Log in LLM Fine-tuning
Jiaru Zou, Yikun Ban, Zihao Li, Yunzhe Qi, Ruizhong Qiu, Ling Yang+, Jingrui He
ICLR 2026Oral Top 1%
Generative Universal Verifier as Multimodal Meta-Reasoner
Xinchen Zhang, Xiaoying Zhang, Youbin Wu, Yanbin Cao, Renrui Zhang, Ruihang Chu, Ling Yang, Yujiu Yang
ICCV 2025Oral Top 1%
Distilling Diffusion Models to Efficient 3D LiDAR Scene Completion
Shengyuan Zhang, An Zhao, Ling Yang, Zejian Li, Chenye Meng, Haoran Xu, Tianrun Chen, AnYang Wei, Perry Pengyun Gu, Lingyun Sun
Core Contributions to Generative Foundations and Applications
NeurIPS 2025
HermesFlow: Seamlessly Closing the Gap in Multimodal Understanding and Generation
Ling Yang+, Xinchen Zhang, Ye Tian, Chenming Shang, Minghao Xu, Wentao Zhang, Bin Cui
ICLR 2025
IterComp: Iterative Composition-Aware Feedback Learning from Model Gallery for Text-to-Image Generation
Xinchen Zhang*, Ling Yang*, Guohao Li, Yaqi Cai, Jiake Xie, Yong Tang, Yujiu Yang, Mengdi Wang, Bin Cui
ICLR 2025
Rectified Diffusion: Straightness Is Not Your Need in Rectified Flow
Fu-Yun Wang, Ling Yang, Zhaoyang Huang, Mengdi Wang, Hongsheng Li
Preprint
Consistency Flow Matching: Defining Straight Flows with Velocity Consistency
Ling Yang+, Zixiang Zhang, Zhilong Zhang, Xingchao Liu, Minkai Xu, Wentao Zhang, Chenlin Meng, Stefano Ermon, Bin Cui
ICLR 2024
Cross-Modal Contextualized Diffusion Models for Text-Guided Visual Generation and Editing
Ling Yang, Zhilong Zhang, Zhaochen Yu, Jingwei Liu, Minkai Xu, Stefano Ermon, Bin Cui
CVPR 2024
Structure-Guided Adversarial Training of Diffusion Models
Ling Yang, Haotian Qian, Zhilong Zhang, Jingwei Liu, Bin Cui
NeurIPS 2023
Improving Diffusion-Based Image Synthesis with Context Prediction
Ling Yang, Jingwei Liu, Shenda Hong, Zhilong Zhang, Zhilin Huang, Zheming Cai, Wentao Zhang, Bin Cui
ACM Computing Surveys 2023
Diffusion Models: A Comprehensive Survey of Methods and Applications
Ling Yang, Zhilong Zhang, Yang Song (OpenAI), Shenda Hong, Runsheng Xu, Yue Zhao, Wentao Zhang, Bin Cui, Ming-Hsuan Yang
NeurIPS 2024
VideoTetris: Towards Compositional Text-to-Video Generation
Ye Tian*, Ling Yang*+, Haotian Yang, Yuan Gao, Yufan Deng, Jingmin Chen, Xintao Wang, Zhaochen Yu, Xin Tao, Pengfei Wan, Di Zhang, Bin Cui
Additional Selected Publications
ICLR 2024
VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPs
Ling Yang, Ye Tian, Minkai Xu, Zhongyi Liu, Shenda Hong, Wei Qu, Wentao Zhang, Bin Cui, Muhan Zhang, Jure Leskovec
CVPR 2020
DPGN: Distribution Propagation Graph Network for Few-Shot Learning
Ling Yang, Liangliang Li, Zilun Zhang, Xinyu Zhou, Erjin Zhou, Yu Liu
ICML 2022Spotlight Top 3%
Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion
Ling Yang+, Shenda Hong
EMNLP 2025Oral Top 1%
EmoAgent: Assessing and Safeguarding Human-AI Interaction for Mental Health Safety
Jiahao Qiu, Yinghui He, Xinzhe Juan, Yimin Wang, Yuhan Liu, Zixin Yao, Yue Wu, Xun Jiang, Ling Yang, Mengdi Wang
2025Talk at ICCV 2025 Workshop on MMRAgI
2025Roundtable forum at WAIC 2025, hosted by Prof. Dahua Lin
π
2025 WAIC Yunfan Award Finalist
10 selected worldwide Β· 2025
π
Outstanding Graduate
Peking University Ph.D. Β· 2025
β
KAUST Rising Stars in AI Symposium
24 selected worldwide Β· 2025
π€
VALSE Distinguished Student Forum
8 selected in China Β· 2024
π
Outstanding Author
Electronics Industry Press Β· 2023
π
National Scholarship for Ph.D. Students
Top 1% at PKU Β· 2022
π‘
Exceptional Award for Academic Innovation
Top 1% at PKU Β· 2022
Program Committee / Reviewer
- ICML, ICLR, CVPR, ICCV, AAAI 2025
- SIGGRAPH, ICML, ICLR, NeurIPS, CVPR, AAAI 2024
- ICML, ICLR, NeurIPS, CVPR, AAAI 2023
- ICML, ICLR, NeurIPS 2022
Journal Reviewer
- ACM Computing Surveys (CSUR)
- IEEE TPAMI
- IEEE TKDE
- IEEE TCSVT
- IEEE TNNLS
- Pattern Recognition (PR)