5 papers
TensorHub: Scalable and Elastic Weight Transfer for LLM RL Training
Chenhao Ye, Huaizheng Zhang, Mingcong Han +11
Modern LLM reinforcement learning (RL) workloads require a highly efficient weight transfer system to scale training across heterogeneous computational resources. However, existing…
OpenView: Empowering MLLMs with Out-of-view VQA
Qixiang Chen, Cheng Zhang, Chi-Wing Fu +2
Recent multimodal large language models (MLLMs) show great potential in natural image understanding. Yet, they perform well, mainly on reasoning in-view contents within the image f…
Delta-SVD: Efficient Compression for Personalized Text-to-Image Models
Tangyuan Zhang, Shangyu Chen, Qixiang Chen +1
Personalized text-to-image models such as DreamBooth require fine-tuning large-scale diffusion backbones, resulting in significant storage overhead when maintaining many subject-sp…
FIXME: Towards End-to-End Benchmarking of LLM-Aided Design Verification
Gwok-Waa Wan, Shengchu Su, Ruihu Wang +15
Despite the transformative potential of Large Language Models (LLMs) in hardware design, a comprehensive evaluation of their capabilities in design verification remains underexplor…
VAU-R1: Advancing Video Anomaly Understanding via Reinforcement Fine-Tuning
Liyun Zhu, Qixiang Chen, Xi Shen +1
Video Anomaly Understanding (VAU) is essential for applications such as smart cities, security surveillance, and disaster alert systems, yet remains challenging due to its demand f…