4.3k citations · 6.9k across the 51 of their papers we have counts for
8 papers · 1 filter
Large Language Models can Learn Rules
Zhaocheng Zhu, Yuan Xue, Xinyun Chen +4
When prompted with a few examples and intermediate steps, large language models (LLMs) have demonstrated impressive performance in various reasoning tasks. However, prompting metho…
Learning Interactive Real-World Simulators
Sherry Yang, Yilun Du, Kamyar Ghasemipour +4
Generative models trained on internet data have revolutionized how text, image, and video content can be created. Perhaps the next milestone for generative models is to simulate re…
Foundation Models for Decision Making: Problems, Methods, and Opportunities
Sherry Yang, Ofir Nachum, Yilun Du +3
Foundation models pretrained on diverse data at scale have demonstrated extraordinary capabilities in a wide range of vision and language tasks. When such models are deployed in re…
Learning Universal Policies via Text-Guided Video Generation
Yilun Du, Mengjiao Yang, Bo Dai +5
A goal of artificial intelligence is to construct an agent that can solve a wide variety of tasks. Recent progress in text-guided image synthesis has yielded models with an impress…
Least-to-Most Prompting Enables Complex Reasoning in Large Language Models
Denny Zhou, Nathanael Schärli, Le Hou +8
Chain-of-thought prompting has demonstrated remarkable performance on various natural language reasoning tasks. However, it tends to perform poorly on tasks which requires solving…
Trust-PCL: An Off-Policy Trust Region Method for Continuous Control
Ofir Nachum, Mohammad Norouzi, Kelvin Xu +1
Trust region methods, such as TRPO, are often used to stabilize policy optimization algorithms in reinforcement learning (RL). While current trust region strategies are effective f…