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Research Interests
My research interests are in RL and agentic systems,
especially in self-evolving coding agent and how we can automate the realistic workflows such as foundational model research, Machine Learning, and Data Analysis.
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Working Experience
AI Research Scientist@Meta SuperIntelligence, 2025 - Present, Agents for automating AI researchers.
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Internship Experience
Intern@Google Deepmind, Meta RL for reasoning & Thinking to Learn.
Intern@Google Deepmind, The evaluation/alignment of Omni-Modality Language Models.
Intern@Google Research & Cloud AI Research The self-improvement of multimodal LLMs.
Intern@NVIDIA ADLR team, Mitigate hackings in RLHF/More Robust Reward Models
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OmnixR: Evaluating Omni-modality Language Models on Reasoning across Modalities
Lichang Chen, Hexiang Hu, Pranav Shyam, Ming-Hsuan Yang, Boqing Gong, et al.
Work Done@Google Deepmind, ICLR 2025
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From Lists to Emojis: How Format Bias Affects Model Alignment
Lichang Chen*, Xuanchang Zhang*, Wei Xiong*, Tianyi Zhou, Heng Huang, Tong Zhang.
ACL, 2025
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RRM: Robust Reward Model Training Mitigates Reward Hacking
Tianqi Liu, Wei Xiong, Jie Ren, Lichang Chen, Tianhe Yu, Mohammad Saleh, et al.
Work Done@Google Deepmind, ICLR 2025
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ODIN: Disentangled Reward Mitigates Hacking in RLHF
Lichang Chen*, Chen Zhu*, Davit Soselio, Tianyi Zhou, Tom Goldstein, Heng Huang, Mohammad Shoeybi, Bryan Catanzaro
ICML, 2024.
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InstructZero: Efficient Instruction Optimization for Black-Box Large Language Models
Lichang Chen*, Jiuhai Chen*, Tom Goldstein, Heng Huang, Tianyi Zhou
ICML, 2024
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AlpaGasus: Training a Better Alpaca with Fewer Data
Lichang Chen*, Shiyang Li*, Jun Yan, Hai Wang, Kalpa Gunaratna, Vikas Yadav, Zheng Tang, Vijay Srinivasan, Tianyi Zhou, Heng Huang, Hongxia Jin
ICLR, 2024
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Backdooring Instruction-Tuned Large Language Models with Virtual Prompt Injection
Jun Yan, Vikas Yadav, Shiyang Li, Lichang Chen, Zheng Tang, Hai Wang, Vijay Srinivasan, Xiang Ren, Hongxia Jin
NAACL , 2024
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HallusionBench: an image-context reasoning benchmark challenging for multi-modality models
Fuxiao Liu, Tianrui Guan, Zongxia Li, Lichang Chen, et al.
CVPR, 2024
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How Many Demonstrations Do You Need for In-context Learning?
Jiuhai Chen, Lichang Chen, Chen Zhu, Tianyi Zhou
EMNLP, 2023
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PTP: Boosting Stability and Performance of Prompt Tuning with Perturbation-Based Regularizer
Lichang Chen, Jiuhai Chen, Heng Huang, Minhao Cheng
EMNLP, 2023
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