Provably Efficient Model-based Policy Adaptation. - Yuda Song 001; Aditi Mavalankar; Wen Sun 002; Sicun Gao (ICML, 2020)

PC-MLP: Model-based Reinforcement Learning with Policy Cover Guided Exploration. - Yuda Song 001; Wen Sun 002 (ICML, 2021)

Transform2Act: Learning a Transform-and-Control Policy for Efficient Agent Design. - Ye Yuan 007; Yuda Song 001; Zhengyi Luo 002; Wen Sun 002; Kris M. Kitani (ICLR, 2022)

Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning approach. - Xuezhou Zhang; Yuda Song 001; Masatoshi Uehara; Mengdi Wang; Alekh Agarwal; Wen Sun 002 (ICML, 2022)

Online No-regret Model-Based Meta RL for Personalized Navigation. - Yuda Song 001; Ye Yuan 007; Wen Sun 002; Kris Kitani (L4DC, 2022)

Representation Learning for General-sum Low-rank Markov Games. - Chengzhuo Ni; Yuda Song 001; Xuezhou Zhang; Chi Jin 001; Mengdi Wang (CoRR, 2022)

Provable Benefits of Representational Transfer in Reinforcement Learning. - Alekh Agarwal; Yuda Song 001; Wen Sun 002; Kaiwen Wang; Mengdi Wang; Xuezhou Zhang (COLT, 2023)

Hybrid RL: Using both offline and online data can make RL efficient. - Yuda Song 001; Yifei Zhou; Ayush Sekhari; Drew Bagnell; Akshay Krishnamurthy; Wen Sun 002 (ICLR, 2023)

Representation Learning for Low-rank General-sum Markov Games. - Chengzhuo Ni; Yuda Song 001; Xuezhou Zhang; Zihan Ding; Chi Jin 001; Mengdi Wang (ICLR, 2023)

The Virtues of Laziness in Model-based RL: A Unified Objective and Algorithms. - Anirudh Vemula; Yuda Song 001; Aarti Singh; Drew Bagnell; Sanjiban Choudhury (ICML, 2023)

Offline Data Enhanced On-Policy Policy Gradient with Provable Guarantees. - Yifei Zhou; Ayush Sekhari; Yuda Song 001; Wen Sun 002 (ICLR, 2024)

Hybrid Reinforcement Learning from Offline Observation Alone. - Yuda Song 001; Drew Bagnell; Aarti Singh (ICML, 2024)

Rich-Observation Reinforcement Learning with Continuous Latent Dynamics. - Yuda Song 001; Lili Wu; Dylan J. Foster; Akshay Krishnamurthy (ICML, 2024)

The Importance of Online Data: Understanding Preference Fine-tuning via Coverage. - Yuda Song 001; Gokul Swamy; Aarti Singh; J. Andrew Bagnell; Wen Sun 002 (NeurIPS, 2024)

Understanding Preference Fine-Tuning Through the Lens of Coverage. - Yuda Song 001; Gokul Swamy; Aarti Singh; J. Andrew Bagnell; Wen Sun 002 (CoRR, 2024)

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models. - Yuda Song 001; Hanlin Zhang 002; Carson Eisenach; Sham M. Kakade; Dean P. Foster; Udaya Ghai (ICLR, 2025)

Accelerating Unbiased LLM Evaluation via Synthetic Feedback. - Zhaoyi Zhou; Yuda Song 001; Andrea Zanette (CoRR, 2025)