Short Bio

I am a final year PhD student at Hong Kong University of Science and Technology, supervised by Prof. Jing Tang. My research interests are developing Efficient Generative Models, and building Few-Step Text-to-Image/Video Diffusion Models for high-quality and real-time generation. I am also interested in Graph Neural Networks.

Academic Service: reviewer for ICML, ICLR, NeurIPS, CVPR, ICCV, etc.

I am expecting to graduate in December 2025 and am actively seeking opportunities in industrial research roles within China and the broader Asian region. If you are interested in discussing potential opportunities, please don’t hesitate to reach out.

If you’re interested in collaborating or exploring potential research opportunities, please don’t hesitate to reach out (带带哥们).

News

[04/2025] Delivered an invited talk at ByteDance about Efficient Post-Training of Diffusion Models (Diffusion Distilattion and RLHF).
[03/2025] We release R0, a novel conditional generation approach via regularized reward maximization. R0 is the first RLHF algorithm that enables post-training diffusion to few-step text-to-image generator without relying on diffusion distillation or images.
[03/2025] We release TDM, unifying the trajectory distillation and distribution. TDM can distill pre-trained DMs to a few-step generator with better performance with extremely low cost. In particular, TDM distills PixArt to a 4-step generator with 2 A800 hours and outperforms teacher regarding real user preference.
[03/2025] We release JDM, a method can add additional control unkown to teacher to one-step student. Moreover, JDM decouples the condition learning and fidelity learning, enabling improved usage of classifier-free guidance (CFG) and seamless integration of human feedback learning (HFL).
[01/2025] Two first/co-first author papers accepted to ICLR 2025.

Publications

(^* denotes co-first authors)

Rewards Are Enough for Fast Photo-Realistic Text-to-image Generation
Yihong Luo, Tianyang Hu, Weijian Luo, Kenji Kawaguchi, Jing Tang
Preprint, 2025
[Paper]

Learning Few-Step Diffusion Models by Trajectory Distribution Matching
Yihong Luo, Tianyang Hu, Jiacheng Sun, Yujun Cai, Jing Tang
Preprint, 2025
[Paper]

Adding Additional Control to One-Step Diffusion with Joint Distribution Matching
Yihong Luo, Tianyang Hu, Yifan Song, Jiacheng Sun, Zhenguo Li, Jing Tang
Preprint, 2025
[Paper]

You Only Sample Once: Taming One-Step Text-to-Image Synthesis by Self-Cooperative Diffusion GANs
Yihong Luo, Xiaolong Chen, Xinghua Qu, Tianyang Hu, Jing Tang
ICLR 2025
[Paper]

Decoupled Graph Energy-based Model for Node Out-of-Distribution Detection on Heterophilic Graphs
Yuhan Chen^*, Yihong Luo^*, Yifan Song, Pengwen Dai, Jing Tang, and Xiaochun Cao
ICLR 2025
[Paper]

Energy-Calibrated VAE with Test Time Free Lunch
Yihong Luo, Siya Qiu, Xingjian Tao, Yujun Cai, Jing Tang
ECCV 2024
[Paper]

Fast Graph Sharpness-Aware Minimization for Enhancing and Accelerating Few-Shot Node Classification
Yihong Luo^*, Yuhan Chen^*, Siya Qiu, Yiwei Wang, Chen Zhang, Yan Zhou, Xiaochun Cao, Jing Tang
NeurIPS 2024
[Paper]

TO-FLOW: Efficient Continuous Normalizing Flows with Temporal Optimization adjoint with Moving Speed
Shian Du^*, Yihong Luo^*, Wei Chen^*, Jian Xu, Delu Zeng
CVPR 2022
[Paper]

LSGNN: Towards General Graph Neural Network in Node Classification by Local Similarity
Yuhan Chen^*, Yihong Luo^*, Jing Tang, Liang Yang, Siya Qiu, Chuan Wang, Xiaochun Cao
IJCAI 2023
[Paper]

SteadySeg: Improving Maritime Trajectory Staging by Steadiness Recognition
Siya Qiu, Yihong Luo, Qiong Luo, Jing Tang
Ocean Engineering (JCR Q1)
[Paper]

Education

2022 — 2025/12 (expected): Ph.D. in Data Science and Analytics, Hong Kong University of Science and Technology
2016 — 2020: B.Sc. in Information Management and Information System, South China University of Technology

Experience

Diffusion Distillation Research Intern, Huawei Noah's Ark Lab
Research Assistant, Hong Kong University of Science and Technology