5 citations · 5 across the 4 of their papers we have counts for
4 papers
Robust Instruction Compliance in Cooperative Multi-Agent Reinforcement Learning
Wo Wei Lin, Ethan Rathbun, Enrico Marchesini +1
Multi-agent reinforcement learning (MARL) in real-world use cases may need to adapt to external natural language instructions that interrupt ongoing behavior and conflict with long…
Beware Untrusted Simulators -- Reward-Free Backdoor Attacks in Reinforcement Learning
Ethan Rathbun, Wo Wei Lin, Alina Oprea +1
Simulated environments are a key piece in the success of Reinforcement Learning (RL), allowing practitioners and researchers to train decision making agents without running expensi…
UI-TARS-2 Technical Report: Advancing GUI Agent with Multi-Turn Reinforcement Learning
Haoming Wang, Haoyang Zou, Huatong Song +109
The development of autonomous agents for graphical user interfaces (GUIs) presents major challenges in artificial intelligence. While recent advances in native agent models have sh…
UI-TARS: Pioneering Automated GUI Interaction with Native Agents
Yujia Qin, Yining Ye, Junjie Fang +32
This paper introduces UI-TARS, a native GUI agent model that solely perceives the screenshots as input and performs human-like interactions (e.g., keyboard and mouse operations). U…