Abstract
線上會議連結：
Webex 會議連結
會議號： 2519 308 4716
密碼： ZFsSXTyv723
The first half of this talk covers the basics of LLMs and the uses of Reinforcement Learning in training LLMs. The second half of this talk dives into the frontier research of embodied AI generalization and the role of LLMs, RL, and large-scale imitation learning in it.
Webex 會議連結
會議號： 2519 308 4716
密碼： ZFsSXTyv723
The first half of this talk covers the basics of LLMs and the uses of Reinforcement Learning in training LLMs. The second half of this talk dives into the frontier research of embodied AI generalization and the role of LLMs, RL, and large-scale imitation learning in it.
Bio
Kuang-Huei Lee is a Staff Research Scientist at Google DeepMind in San Francisco. His research interests center around creating general cognitive agents in both physical and virtual worlds, and his current research spans deep generative models, reasoning, planning, reinforcement learning, and robotics. Prior to joining Google in 2019, Kuang-Huei spent 3 years at Microsoft. He received his graduate degree in Computer Science from Carnegie Mellon University, and his undergraduate degree in Mechanical Engineering from National Taiwan University. His research has been widely published, appearing in venues such as NeurIPS, ICML, ICLR, RSS, IROS, CoRL, CVPR, ECCV, and EMNLP.