12 citations · 18 across the 14 of their papers we have counts for
14 papers
Rollout Efficiency in Reinforcement Learning for Reasoning Large Language Models: A Taxonomy and Future Directions
Niloofar Gholipour, Marcos Assuncao, Gursimran Singh +8
Reasoning-oriented reinforcement learning enables large language models to solve mathematical, coding, and other multi-step tasks, but shifts a substantial portion of the training…
MARLaaS: Multi-Tenant Asynchronous Reinforcement Learning as a Service
Timothy Tin Long Yu, Gursimran Singh, Ge Shi +3
Reinforcement Learning from Verifiable Rewards (RLVR) has significantly improved the reasoning capabilities of large language models (LLMs), particularly in multi-turn agentic sett…
ReviveMoE: Fast Recovery for Hardware Failures in Large-Scale MoE LLM Inference Deployments
Haley Li, Xinglu Wang, Cong Feng +12
As LLM deployments scale over more hardware, the probability of a single failure in a system increases significantly, and cloud operators must consider robust countermeasures to ha…
DECKBench: Benchmarking Multi-Agent Frameworks for Academic Slide Generation and Editing
Daesik Jang, Morgan Lindsay Heisler, Linzi Xing +5
Automatically generating and iteratively editing academic slide decks requires more than document summarization. It demands faithful content selection, coherent slide organization,…
MEPIC: Memory Efficient Position Independent Caching for LLM Serving
Qian Wang, Zahra Yousefijamarani, Morgan Lindsay Heisler +8
Modern LLM applications such as deep-research assistants, coding agents, and Retrieval-Augmented Generation (RAG) systems, repeatedly process long prompt histories containing share…
ElasticMoE: An Efficient Auto Scaling Method for Mixture-of-Experts Models
Gursimran Singh, Timothy Yu, Haley Li +7
Mixture-of-Experts (MoE) models promise efficient scaling of large language models (LLMs) by activating only a small subset of experts per token, but their parallelized inference p…
ExpertWeave: Efficiently Serving Expert-Specialized Fine-Tuned Adapters at Scale
Ge Shi, Hanieh Sadri, Qian Wang +4
Expert-Specialized Fine-Tuning (ESFT) adapts Mixture-of-Experts (MoE) large language models to enhance their task-specific performance by selectively tuning the top-activated exper…
HFX: Joint Design of Algorithms and Systems for Multi-SLO Serving and Fast Scaling
Zahra Yousefijamarani, Xinglu Wang, Qian Wang +12
Large language model (LLM) serving faces the dual challenge of meeting strict user-specific service-level objectives (SLOs) while minimizing computational cost under dynamic, multi…
Huawei Cloud Model-as-a-Service on the CloudMatrix384 SuperPod
Ao Xiao, Bangzheng He, Baoquan Zhang +125
Scaled-out MoE LLMs and scaled-up SuperPods create new systems challenges for production Model-as-a-Service (MaaS), requiring disaggregation, low-latency communication, and decentr…
Efficiently Serving Large Multimodal Models Using EPD Disaggregation
Gursimran Singh, Xinglu Wang, Yifan Hu +9
Large Multimodal Models (LMMs) extend Large Language Models (LLMs) by handling diverse inputs such as images, audio, and video, but at the cost of adding a multimodal encoding stag…
Learn2Aggregate: Supervised Generation of Chvátal-Gomory Cuts Using Graph Neural Networks
Arnaud Deza, Elias B. Khalil, Zhenan Fan +2
We present , a machine learning (ML) framework for optimizing the generation of Chvátal-Gomory (CG) cuts in mixed integer linear programming (MILP). The f…
Machine Learning Insides OptVerse AI Solver: Design Principles and Applications
Xijun Li, Fangzhou Zhu, Hui-Ling Zhen +23
In an era of digital ubiquity, efficient resource management and decision-making are paramount across numerous industries. To this end, we present a comprehensive study on the inte…
Artificial Intelligence for Operations Research: Revolutionizing the Operations Research Process
Zhenan Fan, Bissan Ghaddar, Xinglu Wang +3
The rapid advancement of artificial intelligence (AI) techniques has opened up new opportunities to revolutionize various fields, including operations research (OR). This survey pa…
Achieving Model Fairness in Vertical Federated Learning
Changxin Liu, Zhenan Fan, Zirui Zhou +4
Vertical federated learning (VFL) has attracted greater and greater interest since it enables multiple parties possessing non-overlapping features to strengthen their machine learn…