Fu-Yun Wang (IPA: [fu˧˥ yn˧˥ wɑŋ]) is a final year Ph.D. candidate at MMLab@CUHK. He works on post-training. Currently vibing with world model and agentic stuff.
2027 job market · open to industry research opportunities.
Recent Work New
mni: Context Unrolling in mni ModelsLatest Posts
Scaling Long-Context Attention I: Exact Blocks, Rings, and Sparsity
A unified view of block attention: FlashAttention, Ring Attention, and sparse attention share the same blockwise computation but obey different contracts.
Distributional Distillation I: From a Marginal KL to Two Scores
A first-principles derivation of distributional distillation, from marginal KL objectives to two-score and f-divergence gradients.
Distributional Distillation II: What the Methods Actually Estimate
A method-by-method map of what SDS, VSD, Diff-Instruct, DMD, DMD2, and Phased DMD actually estimate.
Distributional Distillation III: From Scores to Particle Flows
From score differences to particle motion, connecting DMD, Drifting Models, and the Sinkhorn-based W-Flow through Wasserstein gradient-flow geometry.
Theoretical Analysis of ConsistencySolver
A numerical-analysis view of ConsistencySolver, deriving its learnable update rule from PF-ODEs and linear multistep methods.
Theoretical Analysis of Rectified Diffusion
An optimal-transport analysis extending rectification, path straightening, and O(1/K) convergence from linear flows to general diffusion schedules.
On the Equivalence of Consistency Models and MeanFlow
A continuous-time derivation showing that consistency training reduces to MeanFlow, with an extension from one-step CM to multi-step CTM.
Research Summary
Interactive tree diagram of my research. Click nodes to expand/collapse; click paper titles to visit links.
Research Directions
Internship Experience
ByteDance Seed Current
Research Intern · 2025.10 - Present
Video Generation, Multimodal Models
Mentor: Haoqi Fan
Reve Inc
Research Intern · 2025.6 - 2025.11
Multimodal LMs, Diffusion Models, RL
Supervised by: Dr. Han Zhang
Google DeepMind
Research Intern · 2025.2 - 2025.5
Diffusion Distillation, RL
Supervised by: Dr. Long Zhao, Dr. Ting Liu, Dr. Hao Zhou, Dr. LiangZhe Yuan
Collaborated with Prof. Bohyung Han, Prof. Boqing Gong
Avolution AI Acquired by MiniMax
Research Collaboration · 2023.10 - 2024.10
Video Diffusion, Distillation
Collaborated with: Dr. Zhaoyang Huang, Dr. Xiaoyu Shi, Weikang Bian
Tencent AI Lab
Research Intern · 2022.6 - 2022.12
Continual Learning
Supervised by: Dr. Liu Liu, Prof. Yatao Bian
Education
The Chinese University of Hong Kong
Ph.D. in Engineering · 2023 - Present
Supervisor: Prof. Hongsheng Li & Prof. Xiaogang Wang
Nanjing University
B.Eng. in AI (Rank 2/88) · 2019 - 2023
Supervisor: Prof. Han-Jia Ye & Prof. Da-Wei Zhou
Selected Publications
Categorized by theme. Full list on Google Scholar.
Diffusion Post-Training: Acceleration & Reinforcement Learning
Generative Vision Applications
Continual Learning
More
Talks
- TechBeat · Oct 2024
- Kuaishou Kling · Dec 2024
- Nanjing University · Jan 2025
- Talk video · Chinese
Awards
- 2026 JQ Investments ICML Travel Award · PromptRL
- 2026 Tencent Qingyun Travel Grant · ConsistencySolver
- 2025 CVPR Outstanding Reviewer
- 2023 HKPFS
Earlier awards
- 2023 Outstanding Graduate of NJU
- 2023 Outstanding Undergraduate Thesis of NJU
- 2022 Sensetime Scholarship
- 2022 Huawei Scholarship
- 2021 National Scholarship
Academic Services
Reviewer for TPAMI, TCSVT, PRL, CVPR, NeurIPS, ICLR, ICML, ECCV, ICCV, BMVC, and SIGGRAPH Asia.
Reviewing years
| CVPR / NeurIPS | 2023–2025 |
| ICLR / ICML | 2024–2025 |
| ECCV / ICCV | 2024 / 2025 |
| BMVC / SIGGRAPH Asia | 2024 / 2025 |