3 citations · 4 across the 4 of their papers we have counts for
4 papers
RMSWeb: Reflection, Failure-Mode Mining, and Salvage-DS for Web Agent Reinforcement Learning
Chengbo Liu, Lifang Zhou, Ruijie Yan +8
Compact web agents can reduce deployment cost, but training them poses challenges in both data collection and post-SFT reinforcement learning (RL). Successful trajectories are expe…
DeepFusion: Accelerating MoE Training via Federated Knowledge Distillation from Heterogeneous Edge Devices
Songyuan Li, Jia Hu, Ahmed M. Abdelmoniem +3
Recent Mixture-of-Experts (MoE)-based large language models (LLMs) such as Qwen-MoE and DeepSeek-MoE are transforming generative AI in natural language processing. However, these m…
Incentivizing Multi-Tenant Split Federated Learning for Foundation Models at the Network Edge
Songyuan Li, Jia Hu, Geyong Min +1
Foundation models (FMs) such as GPT-4 exhibit exceptional generative capabilities across diverse downstream tasks through fine-tuning. Split Federated Learning (SFL) facilitates pr…
Dynamic Pricing for On-Demand DNN Inference in the Edge-AI Market
Songyuan Li, Jia Hu, Geyong Min +2
The convergence of edge computing and Artificial Intelligence (AI) gives rise to Edge-AI, which enables the deployment of real-time AI applications at the network edge. A key resea…