1 citations · 1 across the 6 of their papers we have counts for
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COBALT: Contextual Bandit Learning with Offline Trajectories for Multi-Turn Code Generation
Ziru Chen, Dongdong Chen, Ruinan Jin +3
Training large language models (LLMs) for multi-turn code generation typically relies on online reinforcement learning (RL), in which models learn from repeatedly generated online…
Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning
Xianke Qiang, Hongda Liu, Xinran Zhang +2
Large Artificial Intelligence Models (LAMs) powered by massive datasets, extensive parameter scales, and extensive computational resources, leading to significant transformations a…
AIGC-assisted Federated Learning for Edge Intelligence: Architecture Design, Research Challenges and Future Directions
Xianke Qiang, Zheng Chang, Ying-Chang Liang
Federated learning (FL) can fully leverage large-scale terminal data while ensuring privacy and security, and is considered as a distributed alternative for the centralized machine…
A Retention-Centric Framework for Continual Learning with Guaranteed Model Developmental Safety
Gang Li, Wendi Yu, Yao Yao +4
In real-world applications, learning-enabled systems often undergo iterative model development to address challenging or emerging tasks, which involve collecting new data, training…