4 papers
Fewer Steps, Better Actions: Rethinking Flow-Matching Inference for VLA Policies
Zhipeng Tang, Xinda Chen, Weining Rao +5
Vision-language-action (VLA) policies based on flow matching generate action chunks through repeated evaluations of an action expert. Increasing the number of integration steps rai…
SpecInF: Exploiting Idle GPU Resources in Distributed DL Training via Speculative Inference Filling
Cunchi Lv, Xiao Shi, Dong Liang +2
Deep Learning (DL), especially with Large Language Models (LLMs), brings benefits to various areas. However, DL training systems usually yield prominent idling GPU resources due to…
Dilu: Enabling GPU Resourcing-on-Demand for Serverless DL Serving via Introspective Elasticity
Cunchi Lv, Xiao Shi, Zhengyu Lei +4
Serverless computing, with its ease of management, auto-scaling, and cost-effectiveness, is widely adopted by deep learning (DL) applications. DL workloads, especially with large l…
Cloudless-Training: A Framework to Improve Efficiency of Geo-Distributed ML Training
Wenting Tan, Xiao Shi1, Cunchi Lv +1
Geo-distributed ML training can benefit many emerging ML scenarios (e.g., large model training, federated learning) with multi-regional cloud resources and wide area network. Howev…