Feng Hong 「洪峰」

I am a final-year Ph.D. candidate at Shanghai Jiao Tong University (SJTU), where I am fortunately co-advised by Prof. Jiangchao Yao, Prof. Ya Zhang and Prof. Yanfeng Wang. I received my bachelor's degree in information engineering from SJTU in 2021.

My research is driven by the goal of making efficient and reliable AI models, including Large Language Models, Diffusion Models, and Multi-modal Models. This technical vision is deeply informed by my practical experience as a research intern at Microsoft Research Asia (MSRA), A*STAR Centre for Frontier AI Research (CFAR), and Meituan Longcat team.

Email: feng.hong [at] sjtu.edu.cn
Open to research collaborations on emerging topics like post-training methods and diffusion LLMs.
Feng Hong

News

Research

For a complete list of my research, please visit Google Scholar.

(* Equal contribution, † Corresponding author)

2026
Rejection Mixing: Fast Semantic Propagation of Mask Tokens for Efficient DLLM Inference Yushi Ye*, Feng Hong*, Huangjie Zheng, Xu Chen, Zhiyong Chen, Yanfeng Wang, Jiangchao Yao. [CVPR 2026]
Focal Reward: Balanced Reinforcement Learning under Rubric-Based Rewards Yu Huang, Zihua Zhao, Zhaoxin Huan, Wanli Gu, Feng Hong, Xinmu Ge, Lin Yuan, Weichang Wu, Qiang Hu, Xiaolu Zhang, Jun Zhou, Jiangchao Yao. [Preprint]
A Comparative Survey of Inference Acceleration for DLLMs against AR-LLMs: No Free Lunch Haoyun Jiang, Junqi He, Muyi Wang, Fanqin Zeng, Feng Hong, Geng Yu, Pengyi Chen, Yushi Ye, Yuting Cao, Yicheng Fu, Ziyi Tang, Haolin Li, Yuchen Xiong, Zhiyong Chen, Xiaofeng Cao, Xiangtao Li, Bo Han, Ya Zhang, Yanfeng Wang, Jiangchao Yao. [Preprint]
Roll Out and Roll Back: Diffusion LLMs are Their Own Efficiency Teachers Fanqin Zeng*, Feng Hong*, Geng Yu, Huangjie Zheng, Xiaofeng Cao, Ya Zhang, Bo Han, Yanfeng Wang, Jiangchao Yao. [Preprint]
Dual-granularity Sinkhorn Distillation for Enhanced Learning from Long-Tailed Noisy Data Feng Hong*, Yu Huang*, Zihua Zhao, Zhihan Zhou, Jiangchao Yao, Dongsheng Li, Ya Zhang, Yanfeng Wang. [MLJ 2026]
Improving Diffusion Models for Class-imbalanced Training Data via Capacity Manipulation Feng Hong, Jiangchao Yao, Yifei Shen, Dongsheng Li, Ya Zhang, Yanfeng Wang. [ICLR 2026 (Oral)]
Wide-In, Narrow-Out: Revokable Decoding for Efficient and Effective DLLMs Feng Hong*, Geng Yu*, Yushi Ye, Haicheng Huang, Huangjie Zheng, Ya Zhang, Yanfeng Wang, Jiangchao Yao. [ICLR 2026] [NeurIPS 2025 ER Workshop (Spotlight)]
TriSpec: Ternary Speculative Decoding via Lightweight Proxy Verification Haoyun Jiang, Junqi He, Feng Hong, Xinlong Yang, Jianwei Zhang, Zheng Li, Zhengyang Zhuge, Zhiyong Chen, Bo Han, Junyang Lin, Jiangchao Yao. [Preprint]
2025
Long-tailed Recognition with Model Rebalancing Jiaan Luo*, Feng Hong*, Timothy Hu, Xiaofeng Cao, Feng Liu, Jiangchao Yao. [NeurIPS 2025]
Learning to Instruct for Visual Instruction Tuning Zhihan Zhou*, Feng Hong*, Jiaan Luo, Jiangchao Yao, Dongsheng Li, Bo Han, Ya Zhang, Yanfeng Wang. [NeurIPS 2025]
Differential-informed Sample Selection Accelerates Multimodal Contrastive Learning Zihua Zhao*, Feng Hong* , Mengxi Chen, Pengyi Chen, Benyuan Liu, Jiangchao Yao, Ya Zhang, Yanfeng Wang. [ICCV 2025]
Diversified Experience Replay for Multi-Agent Reinforcement Learning Guangchong Zhou, Feng Hong , Zeren Zhang, Guoliang Fan. [IJCAI 2025 GAAMAL Workshop]
Innovator: Scientific Continued Pretraining with Fine-grained MoE Upcycling Ning Liao, Xiaoxing Wang, Zehao Lin, Weiyang Guo, Feng Hong, Shixiang Song, Geng Yu, Zihua Zhao, Sitao Xie, Longxuan Wei, Xiangqi Jin, Xiaohan Qin, Jiale Ma, Kai Chen, Jiangchao Yao, Zhouhan Lin, Junchi Yan, Zhiyu Li, Feiyu Xiong, Yanfeng Wang, Linfeng Zhang. [Technical Report 2025]
Uncover the Balanced Geometry in Long-Tailed Contrastive Language-Image Pretraining Zhihan Zhou, Yushi Ye, Feng Hong, Peisen Zhao, Jiangchao Yao, Ya Zhang, Qi Tian, Yanfeng Wang. [MLJ 2025]
2024
Revive Re-weighting in Imbalanced Learning by Density Ratio Estimation Jiaan Luo*, Feng Hong*, Jiangchao Yao, Bo Han, Ya Zhang, Yanfeng Wang. [NeurIPS 2024]
Towards long-tailed, multi-label disease classification from chest X-ray Gregory Holste, Yiliang Zhou, Song Wang, Ajay Jaiswal, Mingquan Lin, Sherry Zhuge, Yuzhe Yang, Dongkyun Kim, Trong-Hieu Nguyen-Mau, Minh-Triet Tran, Jaehyup Jeong, Wongi Park, Jongbin Ryu, Feng Hong, Arsh Verma, Yosuke Yamagishi, Changhyun Kim, Hyeryeong Seo, Myungjoo Kang, Leo Anthony Celi, Zhiyong Lu, Ronald M Summers, George Shih, Zhangyang Wang, Yifan Peng. [MedIA 2024]
Balanced Destruction-Reconstruction Dynamics for Memory-replay Class Incremental Learning Yuhang Zhou, Jiangchao Yao, Feng Hong, Ya Zhang, Yanfeng Wang. [TIP 2024]
UniChest: Conquer-and-Divide Pre-training for Multi-Source Chest X-Ray Classification Tianjie Dai, Ruipeng Zhang, Feng Hong, Jiangchao Yao, Ya Zhang, Yanfeng Wang. [TMI 2024]
Diversified Batch Selection for Training Acceleration Feng Hong, Yueming Lyu, Jiangchao Yao, Ya Zhang, Ivor W Tsang, Yanfeng Wang. [ICML 2024]
On Harmonizing Implicit Subpopulations Feng Hong, Jiangchao Yao, Yueming Lyu, Zhihan Zhou, Ivor Tsang, Ya Zhang, Yanfeng Wang. [ICLR 2024]
2023
Combating Representation Learning Disparity with Geometric Harmonization Zhihan Zhou, Jiangchao Yao, Feng Hong, Ya Zhang, Bo Han, Yanfeng Wang. [NeurIPS 2023 (Spotlight)]
Bag of Tricks for Long-Tailed Multi-Label Classification on Chest X-Rays Feng Hong*, Tianjie Dai*, Jiangchao Yao, Ya Zhang, Yanfeng Wang. [ICCV 2023 CVAMD Workshop]
Long-Tailed Partial Label Learning via Dynamic Rebalancing Feng Hong, Jiangchao Yao, Zhihan Zhou, Ya Zhang, Yanfeng Wang. [ICLR 2023]