6 citations · 8 across the 9 of their papers we have counts for
9 papers
CIDER: Continual Interactive Distillation for Embodied Reinforcement Learning
Houlin Li, Minghui Xu, Guo Xu +8
Human-in-the-loop real-world reinforcement learning enables rapid acquisition of effective robotic manipulation policies for individual tasks, often within tens of minutes. Yet it…
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…
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…
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…
SynthCoder: Anti-pattern identification and model training for FIM mode code completion
Dongjun Yu, Xiao Yan, Zhenrui Li +6
As a leading application of large language models (LLMs) in software engineering, Fill-in-the-Middle (FIM) mode code completion has drawn wide attention. Training such models requi…
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…
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…
Large Language Models for Social Networks: Applications, Challenges, and Solutions
Jingying Zeng, Richard Huang, Waleed Malik +6
Large Language Models (LLMs) are transforming the way people generate, explore, and engage with content. We study how we can develop LLM applications for online social networks. De…
Let AI Entertain You: Increasing User Engagement with Generative AI and Rejection Sampling
Jingying Zeng, Jaewon Yang, Waleed Malik +3
While generative AI excels in content generation, it does not always increase user engagement. This can be attributed to two main factors. First, generative AI generates content wi…