Scholar
Zhengyang Tang
Google Scholar ID: 2RRV0PQAAAAJ
CUHKSZ
Large Language Models
Mathematical Reasoning
Information Retrieval
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Citations & Impact
All-time
Citations
3,963
H-index
8
i10-index
8
Publications
13
Co-authors
9
list available
Contact
Email
zhengyangtang@link.cuhk.edu.cn
CV
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Twitter
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GitHub
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Publications
15 items
PhoneWorld: Scaling Phone-Use Agent Environments
2026
Cited
0
The Missing Piece in Pre-trained Model Evaluation: Reward-Guided Decoding Unlocks Task-Oriented Behavior Without Parameter Updates
2026
Cited
0
Safe, or Simply Incapable? Rethinking Safety Evaluation for Phone-Use Agents
2026
Cited
0
Claw-Eval-Live: A Live Agent Benchmark for Evolving Real-World Workflows
2026
Cited
0
AlphaInventory: Evolving White-Box Inventory Policies via Large Language Models with Deployment Guarantees
2026
Cited
0
Cut Your Losses! Learning to Prune Paths Early for Efficient Parallel Reasoning
2026
Cited
0
Do Phone-Use Agents Respect Your Privacy?
2026
Cited
0
Teaching Language Models to Reason with Tools
2025
Cited
0
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Resume (English only)
Academic Achievements
NeurIPS 2025: CoRT (Code-integrated Reasoning within Thinking)
COLM 2025: SCRIT (Self-Evolving Critique Abilities in LLMs)
ICML 2024: MathScale (Scaling Instruction Tuning for Mathematical Reasoning)
TMLR 2025: GLAN (Generalized Instruction Tuning for Language Models)
ACL 2025: Second Language (Arabic) Acquisition of LLMs via Progressive Vocabulary Expansion (Oral & Panel)
COLING 2022: DPTDR (Deep Prompt Tuning for Dense Passage Retrieval)
Operations Research 2025: ORLM (Customizable Framework for Automated Optimization Modeling)
Contributed to Qwen3 Technical Report (Tool-integrated Reasoning)
Background
Ph.D. candidate at The Chinese University of Hong Kong, Shenzhen, advised by Prof. Benyou Wang.
Research focuses on developing intelligent agents capable of complex reasoning and self-improvement.
Pioneers agentic frameworks leveraging reinforcement learning (RL) for tool-integrated tasks.
Proposed SCRIT, a self-evolving critique model serving as a generative reward model for scalable, supervision-free oversight.
Designed novel instruction tuning frameworks—MathScale, GLAN, and ALAN—for scalable high-quality training data generation.
Also works on efficient information access methods (e.g., DPTDR), achieving top performance on competitive benchmarks.
Co-authors
9 total
Benyou Wang
Assistant Professor, The Chinese University of Hong Kong, Shenzhen
Xingxing Zhang
Microsoft Research
Furu Wei
Distinguished Scientist, Microsoft Research
Ziniu Li
The Chinese University of Hong Kong, Shenzhen
Dayiheng Liu (刘大一恒)
Qwen Team, Alibaba Group
Junyang Lin
Qwen Team, Alibaba Group & Peking University
Li Dong
Microsoft Research
Chengpeng Li
USTC
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