Provably Efficient Policy Gradient Methods for Two-Player Zero-Sum Markov Games. - Yulai Zhao 002; Yuandong Tian; Jason D. Lee; Simon S. Du (CoRR, 2021)

Provably Efficient Policy Optimization for Two-Player Zero-Sum Markov Games. - Yulai Zhao 002; Yuandong Tian; Jason D. Lee; Simon S. Du (AISTATS, 2022)

Optimizing the Performative Risk under Weak Convexity Assumptions. - Yulai Zhao 002 (CoRR, 2022)

Blessing of Class Diversity in Pre-training. - Yulai Zhao 002; Jianshu Chen; Simon S. Du (AISTATS, 2023)

Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement Learning. - Yulai Zhao 002; Zhuoran Yang; Zhaoran Wang 001; Jason D. Lee (ICML, 2023)

Provably Efficient CVaR RL in Low-rank MDPs. - Yulai Zhao 002; Wenhao Zhan; Xiaoyan Hu; Ho-fung Leung; Farzan Farnia; Wen Sun 002; Jason D. Lee (ICLR, 2024)

Feedback Efficient Online Fine-Tuning of Diffusion Models. - Masatoshi Uehara; Yulai Zhao 002; Kevin Black; Ehsan Hajiramezanali; Gabriele Scalia; Nathaniel Lee Diamant; Alex M. Tseng; Sergey Levine; Tommaso Biancalani (ICML, 2024)

Bridging Model-Based Optimization and Generative Modeling via Conservative Fine-Tuning of Diffusion Models. - Masatoshi Uehara; Yulai Zhao 002; Ehsan Hajiramezanali; Gabriele Scalia; Gökcen Eraslan; Avantika Lal; Sergey Levine; Tommaso Biancalani (NeurIPS, 2024)

Fine-Tuning of Continuous-Time Diffusion Models as Entropy-Regularized Control. - Masatoshi Uehara; Yulai Zhao 002; Kevin Black; Ehsan Hajiramezanali; Gabriele Scalia; Nathaniel Lee Diamant; Alex M. Tseng; Tommaso Biancalani; Sergey Levine (CoRR, 2024)

Adding Conditional Control to Diffusion Models with Reinforcement Learning. - Yulai Zhao 002; Masatoshi Uehara; Gabriele Scalia; Tommaso Biancalani; Sergey Levine; Ehsan Hajiramezanali (CoRR, 2024)

Understanding Reinforcement Learning-Based Fine-Tuning of Diffusion Models: A Tutorial and Review. - Masatoshi Uehara; Yulai Zhao 002; Tommaso Biancalani; Sergey Levine (CoRR, 2024)

Derivative-Free Guidance in Continuous and Discrete Diffusion Models with Soft Value-Based Decoding. - Xiner Li; Yulai Zhao 002; Chenyu Wang 003; Gabriele Scalia; Gökcen Eraslan; Surag Nair; Tommaso Biancalani; Aviv Regev; Sergey Levine; Masatoshi Uehara (CoRR, 2024)

Adding Conditional Control to Diffusion Models with Reinforcement Learning. - Yulai Zhao 002; Masatoshi Uehara; Gabriele Scalia; Sun-Yuan Kung; Tommaso Biancalani; Sergey Levine; Ehsan Hajiramezanali (ICLR, 2025)

Inference-Time Alignment in Diffusion Models with Reward-Guided Generation: Tutorial and Review. - Masatoshi Uehara; Yulai Zhao 002; Chenyu Wang 003; Xiner Li; Aviv Regev; Sergey Levine; Tommaso Biancalani (CoRR, 2025)

Reward-Guided Iterative Refinement in Diffusion Models at Test-Time with Applications to Protein and DNA Design. - Masatoshi Uehara; Xingyu Su; Yulai Zhao 002; Xiner Li; Aviv Regev; Shuiwang Ji; Sergey Levine; Tommaso Biancalani (CoRR, 2025)

Iterative Distillation for Reward-Guided Fine-Tuning of Diffusion Models in Biomolecular Design. - Xingyu Su; Xiner Li; Masatoshi Uehara; Sunwoo Kim; Yulai Zhao 002; Gabriele Scalia; Ehsan Hajiramezanali; Tommaso Biancalani; Degui Zhi; Shuiwang Ji (CoRR, 2025)

One Token to Fool LLM-as-a-Judge. - Yulai Zhao 002; Haolin Liu; Dian Yu; S. Y. Kung; Haitao Mi; Dong Yu (CoRR, 2025)