6 papers
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…
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…
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…