4 citations · 5 across the 9 of their papers we have counts for
11 papers
Demonstration-Free Success-Probability Reward Learning for Generalist Robot Policies
Duo Wu, Haifeng Wang, Rongwei Lu +6
Reinforcement learning (RL) enables generalist robot policies to improve through trial-and-error interaction, yet its effectiveness is fundamentally constrained by sparse task rewa…
VVS: Accelerating Speculative Decoding for Visual Autoregressive Generation via Partial Verification Skipping
Haotian Dong, Ye Li, Rongwei Lu +3
Visual autoregressive (AR) generation models have demonstrated strong potential for image generation, yet their next-token-prediction paradigm introduces considerable inference lat…
Accelerating Parallel Diffusion Model Serving with Residual Compression
Jiajun Luo, Yicheng Xiao, Jianru Xu +5
Diffusion models produce realistic images and videos but require substantial computational resources, necessitating multi-accelerator parallelism for real-time deployment. However,…
Taming Latency and Bandwidth: A Theoretical Framework and Adaptive Algorithm for Communication-Constrained Training
Rongwei Lu, Jingyan Jiang, Chunyang Li +2
Regional energy caps limit the growth of any single data center used for large-scale model training. This single-center training paradigm works when model size remains manageable,…
-FedHT: Stepsize-Aware Hard-Threshold Gradient Compression in Federated Learning
Rongwei Lu, Yutong Jiang, Jinrui Zhang +4
Gradient compression can effectively alleviate communication bottlenecks in Federated Learning (FL). Contemporary state-of-the-art sparse compressors, such as Top-, exhibit high…
Beyond A Single AI Cluster: A Survey of Decentralized LLM Training
Haotian Dong, Jingyan Jiang, Rongwei Lu +5
The emergence of large language models (LLMs) has revolutionized AI development, yet the resource demands beyond a single cluster or even datacenter, limiting accessibility to well…
Staleness-Centric Optimizations for Parallel Diffusion MoE Inference
Jiajun Luo, Lizhuo Luo, Jianru Xu +4
Mixture-of-Experts-based (MoE-based) diffusion models demonstrate remarkable scalability in high-fidelity image generation, yet their reliance on expert parallelism introduces crit…
MesonGS: Post-training Compression of 3D Gaussians via Efficient Attribute Transformation
Shuzhao Xie, Weixiang Zhang, Chen Tang +4
3D Gaussian Splatting demonstrates excellent quality and speed in novel view synthesis. Nevertheless, the huge file size of the 3D Gaussians presents challenges for transmission an…
A Joint Approach to Local Updating and Gradient Compression for Efficient Asynchronous Federated Learning
Jiajun Song, Jiajun Luo, Rongwei Lu +3
Asynchronous Federated Learning (AFL) confronts inherent challenges arising from the heterogeneity of devices (e.g., their computation capacities) and low-bandwidth environments, b…
Retraining-free Model Quantization via One-Shot Weight-Coupling Learning
Chen Tang, Yuan Meng, Jiacheng Jiang +5
Quantization is of significance for compressing the over-parameterized deep neural models and deploying them on resource-limited devices. Fixed-precision quantization suffers from…
Data-Aware Gradient Compression for FL in Communication-Constrained Mobile Computing
Rongwei Lu, Yutong Jiang, Yinan Mao +4
Federated Learning (FL) in mobile environments faces significant communication bottlenecks. Gradient compression has proven as an effective solution to this issue, offering substan…