3 papers
cs.DC2026
xLLM Technical Report
Tongxuan Liu, Tao Peng, Peijun Yang +50
We introduce xLLM, an intelligent and efficient Large Language Model (LLM) inference framework designed for high-performance, large-scale enterprise-grade serving, with deep optimi…
cs.LG2025
RLHFSpec: Breaking the Efficiency Bottleneck in RLHF Training via Adaptive Drafting
Siqi Wang, Hailong Yang, Junjie Zhu +3
Reinforcement Learning from Human Feedback (RLHF) is an important fine-tuning technique for large language models (LLMs) and comprises three stages: generation, inference, and trai…
cs.DC2024
Minions: Accelerating Large Language Model Inference with Aggregated Speculative Execution
Siqi Wang, Hailong Yang, Xuezhu Wang +10
Large language models (LLM) have recently attracted surging interest due to their outstanding capabilities across various domains. However, enabling efficient LLM inference is chal…