1 citations · 1 across the 4 of their papers we have counts for
6 papers
VINE: Taming Generative Control Policies for Reinforcement Learning
Rushuai Yang, Zhuo Han, Houlin Li +10
Flow-matching policies have emerged as an effective policy parameterization for robot learning. They iteratively generate actions from noise, enabling highly expressive modeling of…
ALOE: Action-Level Off-Policy Evaluation for Vision-Language-Action Model Post-Training
Rushuai Yang, Hecheng Wang, Zhichao Wu +11
We study how to improve large foundation vision-language-action (VLA) systems through human-in-the-loop reinforcement learning (RL) in real-world environments. A key challenge is l…
FingerTip 20K: A Benchmark for Proactive and Personalized Mobile LLM Agents
Qinglong Yang, Haoming Li, Haotian Zhao +4
Mobile GUI agents are becoming critical tools to improve user experience on smart devices, with multimodal large language models (MLLMs) emerging as the dominant paradigms in this…
GLM-5: from Vibe Coding to Agentic Engineering
GLM-5-Team, :, Aohan Zeng +184
We present GLM-5, a next-generation foundation model designed to transition the paradigm of vibe coding to agentic engineering. Building upon the agentic, reasoning, and coding (AR…
SynthCoder: A Synthetical Strategy to Tune LLMs for Code Completion
Dongjun Yu, Xiao Yan, Zhenrui Li +6
Code completion is a prominent application of Large Language Models (LLMs) in software engineering. Due to the near real-time response requirements of this task, base models with s…
Are We There Yet? A Measurement Study of Efficiency for LLM Applications on Mobile Devices
Xiao Yan, Yi Ding
Recent advancements in large language models (LLMs) have prompted interest in deploying these models on mobile devices to enable new applications without relying on cloud connectiv…