I am a second-year Ph.D. student in the Department of Mathematics at the Courant Institute of Mathematical Sciences, New York University, advised by Qi Lei. My current research interests center on the mechanisms of supervised fine-tuning (SFT) and reinforcement learning (RL) in post-training, and on using mechanistic understanding to improve these methods. I also study chain-of-thought and reasoning in language models, aiming to understand their mechanisms through both theory and experiments. In addition, I am interested in optimization for LLMs and deep learning, as well as AI for mathematics.
*: equal contribution.
ICLR 2026 LIT Workshop, Best Paper Runner-Up
Identifies transition alignment as a key determinant of when chain-of-thought prompting improves sample efficiency, and validates the theory with controlled synthetic benchmarks.
AISTATS, 2025
Recasts data reconstruction as an inverse problem, derives matching reconstruction-error bounds for two-layer networks, and proposes a utility-matched evaluation protocol.
ICLR, 2024 Spotlight
Shows how stochastic gradient dynamics can move from higher-rank minima to lower-rank minima in regularized deep linear networks, while reverse jumps have probability zero.
AISTATS, 2023
Proves that a single model-gradient query at random initialization can identify training samples under broad neural network assumptions, with an efficient tensor-decomposition reconstruction algorithm.