1.2k citations · 2k across the 7 of their papers we have counts for
7 papers
Large Language Models Can Self-Improve
Jiaxin Huang, Shixiang Shane Gu, Le Hou +4
Large Language Models (LLMs) have achieved excellent performances in various tasks. However, fine-tuning an LLM requires extensive supervision. Human, on the other hand, may improv…
Mind's Eye: Grounded Language Model Reasoning through Simulation
Ruibo Liu, Jason Wei, Shixiang Shane Gu +5
Successful and effective communication between humans and AI relies on a shared experience of the world. By training solely on written text, current language models (LMs) miss the…
Scaling Instruction-Finetuned Language Models
Hyung Won Chung, Le Hou, Shayne Longpre +32
Finetuning language models on a collection of datasets phrased as instructions has been shown to improve model performance and generalization to unseen tasks. In this paper we expl…
Deep Billboards towards Lossless Real2Sim in Virtual Reality
Naruya Kondo, So Kuroki, Ryosuke Hyakuta +3
An aspirational goal for virtual reality (VR) is to bring in a rich diversity of real world objects losslessly. Existing VR applications often convert objects into explicit 3D mode…
Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates
Shixiang Gu, Ethan Holly, Timothy Lillicrap +1
Reinforcement learning holds the promise of enabling autonomous robots to learn large repertoires of behavioral skills with minimal human intervention. However, robotic application…
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic
Shixiang Gu, Timothy Lillicrap, Zoubin Ghahramani +2
Model-free deep reinforcement learning (RL) methods have been successful in a wide variety of simulated domains. However, a major obstacle facing deep RL in the real world is their…