About me

Welcome to my personal website! I am a second-year Ph.D. student at the Guanghua School of Management, Peking University. My superviser is Professer Yijie Peng. I focus on BP-free training paradigms, reinforcement learning, and optimization. My work explores novel learning frameworks that move beyond backpropagation, aiming for more efficient and scalable AI training methods. I also have great interest in quantitivate finance.

I hold dual bachelor’s degrees in Mechanical Engineering and Artificial Intelligence from Harbin Institute of Technology in 2023. With a strong engineering background, I am passionate about combining theoretical analysis with practical engineering to develop new learning paradigms and optimize AI-driven decision-making for real-world applications. Feel free to contact me at rtkenny@stu.pku.edu.cn.

Pubilication

Zeroth-order Informed Fine-Tuning for Diffusion Model: A Recursive Likelihood Ratio Optimizer

We propose a recursive likelihood ratio method for fine-tuning diffusion models in a zeroth-order informed manner, improving sample efficiency and generalization across reward landscapes.

Tao Ren, Zishi Zhang, Zehao Li, Jingyang Jiang, Shentao Qin, Guanghao Li, Yan Li, Yi Zheng, Xinping Li, Min Zhan, Yijie Peng. arXiv preprint arXiv:2502.00639, 2025
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FLOPS: Forward Learning with OPtimal Sampling

We introduce FLOPS, a forward learning algorithm with optimal sampling strategies to enhance training efficiency in simulation-based models.

Published in ICLR 2025

Tao Ren, Zishi Zhang, Jinyang Jiang, Guanghao Li, Zeliang Zhang, Mingqian Feng, Yijie Peng. arXiv preprint arXiv:2410.05966, 2024
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