1 citations · 1 across the 5 of their papers we have counts for
5 papers
Does RLHF Scale? Exploring the Impacts From Data, Model, and Method
Zhenyu Hou, Pengfan Du, Yilin Niu +7
This study explores the scaling properties of Reinforcement Learning from Human Feedback (RLHF) in Large Language Models (LLMs). Although RLHF is considered an important step in po…
AndroidLab: Training and Systematic Benchmarking of Android Autonomous Agents
Yifan Xu, Xiao Liu, Xueqiao Sun +7
Autonomous agents have become increasingly important for interacting with the real world. Android agents, in particular, have been recently a frequently-mentioned interaction metho…
WebRL: Training LLM Web Agents via Self-Evolving Online Curriculum Reinforcement Learning
Zehan Qi, Xiao Liu, Iat Long Iong +11
Large language models (LLMs) have shown remarkable potential as autonomous agents, particularly in web-based tasks. However, existing LLM web agents heavily rely on expensive propr…
AutoGLM: Autonomous Foundation Agents for GUIs
Xiao Liu, Bo Qin, Dongzhu Liang +27
We present AutoGLM, a new series in the ChatGLM family, designed to serve as foundation agents for autonomous control of digital devices through Graphical User Interfaces (GUIs). W…
Multi-modal Relation Distillation for Unified 3D Representation Learning
Huiqun Wang, Yiping Bao, Panwang Pan +4
Recent advancements in multi-modal pre-training for 3D point clouds have demonstrated promising results by aligning heterogeneous features across 3D shapes and their corresponding…