Bo Dai

Address

Contact

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I am an assistant professor at CSE in Georgia Tech. I am also affiliated with Google DeepMind as a staff research scientist.

My principal research interest lies on Agent AI upon Generative Models, Representation, and Reinforcement Learning, aiming for creating agents with decision-making and planning ability through modeling the world.

CODA E1342A, 756 W Peachtree St NW, Atlanta, GA 30308

• bodai at cc.gatech.edu    (for general academic work)
• bodai at google.com    (for Google related work)

daibond_alpha

Mar 2023 - NOW

Sep 2018 - NOW

Aug 2013 - Aug 2018

• Assistant Professor, School of Computational Science & Engineering, Georgia Tech

• Staff Research Scientist, Google Brain

• Ph.D., School of Computational Science & Engineering, Georgia Tech

Peer Reviewed Publications

• Representation Learning via Non-Contrastive Mutual Information
  Daniel Guo, Bernardo Avila Pires, Khimya Khetarpal, Dale Schuurmans, Bo Dai
  International Conference on Artificial Intelligence and Statistics (AISTATS) 2026. Spotlight
  [ Paper ]

• MLE-Smith: Scaling MLE Tasks with Automated Multi-agent Pipeline
  Rushi Qiang, Yuchen Zhuang, Anikait Singh, Percy Liang, Chao Zhang, Sherry Yang, Bo Dai
  International Conference on Learning Representations (ICLR) 2026.
  [ Paper ]

• Spectral Bellman Method: Unifying Representation and Exploration in RL
  Ofir Nabati, Shie Mannor, Bo Dai, Guy Tennenholtz
  International Conference on Learning Representations (ICLR) 2026.
  [ Paper ]

• AmorLIP: Efficient Language-Image Pretraining via Amortization
  Haotian Sun, Yitong Li, Yuchen Zhuang, Niao He, Hanjun Dai, Bo Dai
  Advances in Neural Information Processing Systems (NeurIPS) 2025.
  [ Paper ]

• MLE-Dojo: Interactive Environments for Empowering LLM Agents in Machine Learning Engineering
  Rushi Qiang, Yuchen Zhuang, Yinghao Li, Dingu Sagar, Rongzhi Zhang, ChangHao Li, Ian Shu-Hei Wong, Sherry Yang, Percy Liang, Chao Zhang, Bo Dai
  Advances in Neural Information Processing Systems (NeurIPS) 2025.
  [ WebPages ][ Leaderboard ][ Paper ]

• Exploration from a Primal-Dual Lens: Value-Incentivized Actor-Critic Methods for Sample-Efficient Online RL
  Tong Yang, Bo Dai, Lin Xiao, Yuejie Chi
  Advances in Neural Information Processing Systems (NeurIPS) 2025.
  [ Paper ]

• Matryoshka Pilot: Learning to Drive Black-Box LLMs with LLMs
  ChangHao Li, Yuchen Zhuang, Rushi Qiang, Haotian Sun, Hanjun Dai, Chao Zhang, Bo Dai
  Advances in Neural Information Processing Systems (NeurIPS) 2025.
  [ Paper ]

• Efficient Online Reinforcement Learning for Diffusion Policy
  Haitong Ma, Tianyi Chen, Kai Wang, Na Li, Bo Dai
  International Conference on Machine Learning (ICML) 2025.
  [ Paper ]

• Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF
  Shicong Cen, Jincheng Mei, Katayoon Goshvadi, Hanjun Dai, Tong Yang, Sherry Yang, Dale Schuurmans, Yuejie Chi, Bo Dai
  International Conference on Learning Representations (ICLR) 2025.
  [ Paper ]

• Diffusion Spectral Representation for Reinforcement Learning
  Dmitry Shribak, Chen-Xiao Gao, Yitong Li, Chenjun Xiao, Bo Dai
  Advances in Neural Information Processing Systems (NeurIPS) 2024.
  [ Paper ] [ Website ] [ Code ]

• BBox-Adapter: Lightweight Adapting for Black-Box Large Language Models
  Haotian Sun, Yuchen Zhuang, Wei Wei, Chao Zhang, Bo Dai
  International Conference on Machine Learning (ICML) 2024. Spotlight
  [ Paper ] [ Website ] [ Code ]

• Provable Representation with Efficient Planning for Partially Observable Reinforcement Learning
  Hongming Zhang, Tongzheng Ren, Chenjun Xiao, Dale Schuurmans, Bo Dai
  International Conference on Machine Learning (ICML) 2024.
  [ Paper ] [ Website ] [ Code ]

• Learning Universal Policies via Text-Guided Video Generation
  Yilun Du*, Mengjiao Yang*, Bo Dai, Hanjun Dai, Ofir Nachum, Joshua B. Tenenbaum, Dale Schuurmans, Pieter Abbeel
  Advances in Neural Information Processing Systems (NeurIPS) 2023. Spotlight
  [ Paper ][ Blog ][ Website ]

• AdaPlanner: Adaptive Planning from Feedback with Language Models
  Haotian Sun, Yuchen Zhuang, Lingkai Kong, Bo Dai, Chao Zhang
  Advances in Neural Information Processing Systems (NeurIPS) 2023.
  [ Paper ][ Code ]

• Stochastic Gradient Succeeds for Bandits
  Jincheng Mei*, Zixin Zhong*, Bo Dai, Alekh Agarwal, Csaba Szepesvari, Dale Schuurmans
  International Conference on Machine Learning (ICML) 2023.
  [ Paper ]

Current Students

Haotian Sun, Georgia Tech CSE PhD

Changhao Li, Georgia Tech CSE PhD

Rushi Qiang, Georgia Tech CSE PhD

Tianyi Chen, Georgia Tech CSE PhD

Chenxiao Gao, Georgia Tech CSE PhD

Edward Chen, Georgia Tech Mathematics Master

Former Members

Uzair Akbar, Georgia Tech CS Master

Aaron Trinh, Georgia Tech Undergraduate

Yitong Li, Georgia Tech CS Master, ML Engineer at TikTok

Jincheng Mei, UAlberta PhD, Research Scientist at Google Brain

Chenjun Xiao, UAlberta PhD, Assistant Professor at CUHK, Shenzhen, and Researcher in Kimi

Zhuangdi Zhu, MSU PhD, Assistant Professor at George Mason University

Tongzheng Ren, UT Austin PhD, Quantitative Researcher at Citadel Securities