Qingyang Zhang

Qingyang Zhang 张庆阳

I am a 2nd-year PhD student at Tianjin University, supervised by Prof. Changqing Zhang.

I received my Bachelor's degree in School of Computer Science from Tianjin University. After that, I pursued my Master's in the School of Computer Science at Tianjin University and transitioned into a PhD candidate through the direct doctoral program (2+4) in 2024.

My research interests include Large Reasoning Models, Out-of-Distribution Detection/Generalization and Multimodal Learning.

Email / Google Scholar / Github / 中文简历 /

News

[2025-09] One paper about Unsupervised LLM Reasoning Incentivization has been accepted by NeurIPS as a Spotlight.

[2025-06] Gave a talk about "Reinforcement Learning with Internal Reward" @ Huawei 2012 Lab. Thanks Dr. Xueyan Niu's invitation!

[2025-01] One paper about test-time adaption has been accepted by ICLR.

[2024-05] One paper about Out-of-distribution Detection has been accepted by NeurIPS.

[2024-05] We release a survey about fusion of low-quality multi-modal data. [arXiv]

[2024-04] Starting an internship at Tencent AI Lab, mentored by Dr. Yatao Bian.

[2023-04] Two paper accepted by ICML including one Oral paper, thanks to all co-authors.

Publications

	Right Question is Already Half the Answer: Fully Unsupervised LLM Reasoning Incentivization Qingyang Zhang, Haitao Wu, Changqing Zhang, Peilin Zhao, Yatao Bian NeurIPS Spotlight, 2025 arXiv / code Incentivizate LLM's reasoning capability without any external supervision (human-verified reasoning trace, golden answer or pretrained reward model).
	COME: Test-time Adaption by Conservatively Minimizing Entropy Qingyang Zhang, Yatao Bian, Xinke Kong, Peilin Zhao and Changqing Zhang ICLR, 2025 arXiv / code A simple learning principle for test time adaption.
	The Best of both Worlds: On the Dilemma of Out-of-Distribution Detection Qingyang Zhang, Qiuxuan Feng, Joey Tianyi Zhou, Yatao Bian, Qinghua Hu and Changqing Zhang NeurIPS, 2024 arXiv / code Solve conflicts between OOD detection and generalization for dual-optimal performance.
	Provable Dynamic Fusion for Low-quality Multimodal Learning Qingyang Zhang, Haitao Wu, Changqing Zhang, Qinghua Hu, Huazhu Fu, Joey Tianyi Zhou, Xi Peng ICML, 2023 arXiv / code Theory-inspired dynimical fuse strategy for quality-varying modalities in real world.
	Calibrating Multimodal Learning Huan Ma, Qingyang Zhang (co-first author), Changqing Zhang, Bingzhe Wu, Huazhu Fu, Joey Tianyi Zhou, Qinghua Hu ICML Oral, 2023 arXiv / code Mitigate the greedy nature of multimodal learning by regularizing the model confidence.

Survey

Multimodal Fusion on Low-quality Data: A Comprehensive Survey

Qingyang Zhang, Yake Wei, Zongbo Han, Huazhu Fu, Xi Peng, Cheng Deng, Qinghua Hu, Cai Xu, Jie Wen, Di Hu, Changqing Zhang

arXiv / awesome list

A systematical survey about fusion of low-quality multi-modal data.

Services

Conference Reviewer: ICLR 2022-2024, NeurIPS 2023-2024, ICML 2024

Awards

National Scholarship (twice, 1%) 2022, 2023

Last updated at Apr. 2025

Thanks Jon Barron for this amazing template.