55 citations · 61 across the 11 of their papers we have counts for
5 papers · 1 filter
SCALECUA: Scaling Computer Use Agents with Verifiable Task Synthesis and Efficient Online RL
Bowen Lv, Xiao Liu, Yanyu Ren +7
Computer use agents (CUAs) are emerging as a powerful interface for automating complex digital workflows through visual perception and GUI execution. Online reinforcement learning…
AgentRL: Scaling Agentic Reinforcement Learning with a Multi-Turn, Multi-Task Framework
Hanchen Zhang, Xiao Liu, Bowen Lv +11
Recent advances in large language models (LLMs) have sparked growing interest in building generalist agents that can learn through online interactions. However, applying reinforcem…
ComputerRL: Scaling End-to-End Online Reinforcement Learning for Computer Use Agents
Hanyu Lai, Xiao Liu, Yanxiao Zhao +7
We introduce ComputerRL, a framework for autonomous desktop intelligence that enables agents to operate complex digital workspaces skillfully. ComputerRL features the API-GUI parad…
VisualAgentBench: Towards Large Multimodal Models as Visual Foundation Agents
Xiao Liu, Tianjie Zhang, Yu Gu +27
Large Multimodal Models (LMMs) have ushered in a new era in artificial intelligence, merging capabilities in both language and vision to form highly capable Visual Foundation Agent…
AgentBench: Evaluating LLMs as Agents
Xiao Liu, Hao Yu, Hanchen Zhang +19
The potential of Large Language Model (LLM) as agents has been widely acknowledged recently. Thus, there is an urgent need to quantitatively \textit{evaluate LLMs as agents} on cha…