SOCIALS

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I also maintain most of my study notes on LLM here.

$$ \Large \textbf {About Me} \\ $$

I’m currently a 4th-year Ph.D. candidate at Department of Computer Science, University of California, Los Angeles (UCLA), where I am very fortunate to be advised by Prof. Wei Wang. Alongside my studies, I'm a student researcher at Google. Earlier, I completed research internships at Microsoft Research and Amazon AWS, and I’m honored to receive the 2025 Amazon Fellowship.

I earned both my B.S. in Mathematics and M.S. in Computer Science from UCLA. During that time, I’ve been an student researcher at UCLA-NLP group with Prof. Kai-Wei Chang.

My current research revolves around post-training strategies for Large Language Models (LLMs), particularly focusing on RL(VR), synthetic data generation, and self-improvement in LLMs. Most recently, I work on multi-modal and agentic reasoning.

Quick links:

My Blogs / Notes

My Talks / Presentations

PROFESSIONAL EXPERIENCE

2025 March-Ongoing

Student Researcher | Google LLC

RL for LLM Agentic Reasoning

Project 1: Ultra-fast Exploration for Scalable Agentic Reasoner [Contribution to Gemma]

Project 2: to be released.

2024 Summer

Research Intern | Microsoft Research

LLM Self-Training for Math Reasoning.

Paper: Flow-DPO: Improving LLM Mathematical Reasoning through Online Multi-Agent Learning

[NeurIPS 2024 Math-AI Workshop] [Media]

2023 Summer

Applied Scientist Intern | Amazon AWS

Large Language Model Reasoning with Knowledge Graphs.

PAPERS (See full list in my Google Scholar) *Equal Contribution

Pre-prints

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• OpenVLThinker: An Early Exploration to Vision-Language Reasoning via Iterative Self-Improvement. Yihe Deng, Hritik Bansal, Fan Yin, Nanyun Peng, Wei Wang, Kai-Wei Chang

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• DuoGuard: A Two-Player RL-Driven Framework for Multilingual LLM Guardrails. Yihe Deng*, Yu Yang*, Junkai Zhang*, Wei Wang, Bo Li

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• MIRAI: Evaluating LLM Agents for Event Forecasting. Chenchen Ye*, Ziniu Hu*, Yihe Deng*, Zijie Huang, Mingyu Derek Ma, Yanqiao Zhu, Wei Wang

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2025

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• Mitigating Object Hallucination in Large Vision-Language Models via Image-Grounded Guidance. [ICML 25’ Spotlight (top 2.6%)] Linxi Zhao*, Yihe Deng*, Weitong Zhang, Quanquan Gu

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2024

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• Enhancing Large Vision Language Models with Self-Training on Image Comprehension. [NeurIPS 24’] Yihe Deng*, Pan Lu*, Fan Yin, Ziniu Hu, Sheng Shen, James Zou, Kai-Wei Chang, Wei Wang

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• Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models. [ICML 24’] Zixiang Chen*, Yihe Deng*, Huizhuo Yuan*, Kaixuan Ji, Quanquan Gu

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• Understanding Transferable Representation Learning and Zero-shot Transfer in CLIP. [ICLR 24’] Zixiang Chen*, Yihe Deng*, Yuanzhi Li, and Quanquan Gu </aside>

Before 2024

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• Robust Learning with Progressive Data Expansion against Spurious Correlation. [NeurIPS 23’] Yihe Deng*, Yu Yang*, Baharan Mirzasoleiman, and Quanquan Gu </aside>