Ziniu Li
About meI am a Ph.D. student at The Chinese University of Hong Kong, Shenzhen (CUHKSZ), advised by Prof. Zhi-Quan (Tom) Luo. I am interested in artificial intelligence, especially reinforcement learning and large language models. I have worked/interned at Tencent, Nanjing University, Cardinal Operations, etc. My curriculum vitae can be downloaded from here. Feel free to contact me if you want to discuss some ideas. Recent Highlights*: indicating equal contribution or alphabetic ordering. Adam-mini: Use Fewer Learning Rates To Gain More TL;DR: This work develops a mini-version of Adam, which cuts down >90% learning rates in Adam based on Hessian structure of LLMs. On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularization TL;DR: This work identifies the preference collapse in RLHF and addresses this issue by a matching regularization ReMax: A Simple, Effective, and Efficient Reinforcement Learning Method for Aligning Large Language Models TL;DR: This work develops an RL method called ReMax for RLHF in LLMs, which is simple (6 lines of code) and efficient (less memory and fast training) When is RL better than DPO in RLHF? A Representation and Optimization Perspective TL;DR: This work analyzes the reward modeling quality in view of representations, and analyzed the optimization error sources Why Transformers Need Adam: A Hessian Perspective Imitation Learning from Imperfection: Theoretical Justifications and Algorithms TL;DR: This work validates that importance sampling is effective in data selection when leveraging multiple imperfect (out-of-distribution and low-quality) data sources ServiceReviewerNeurIPS (Top Reviewer), ICML (Outstanding Reviewer), ICLR (Highlighted Reviewer). Teaching Assistant
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