3 papers
cs.RO2026
EmbodiedMind: Adaptive Data Curation and Prefix-Tree Reinforcement Learning for Efficient Embodied Intelligence
Feifan Wang, Zongbing Zhang, Yu Zhang +8
Training embodied foundation models typically requires massive-scale datasets and extensive computational resources, yet often suffers from three critical limitations: (1) ineffici…
cs.DC2025
Optimizing LLM Inference Throughput via Memory-aware and SLA-constrained Dynamic Batching
Bowen Pang, Kai Li, Feifan Wang
The increasing adoption of large language models (LLMs) necessitates inference serving systems that can deliver both high throughput and low latency. Deploying LLMs with hundreds o…
cs.DC2025
Hybrid Offline-online Scheduling Method for Large Language Model Inference Optimization
Bowen Pang, Kai Li, Ruifeng She +1
With the development of large language models (LLMs), it has become increasingly important to optimize hardware usage and improve throughput. In this paper, we study the inference…