5 citations · 14 across the 12 of their papers we have counts for
6 papers · 1 filter
Blending Supervised and Reinforcement Fine-Tuning with Prefix Sampling
Zeyu Huang, Tianhao Cheng, Zihan Qiu +4
Existing LLMs-post-training techniques are broadly categorized into supervised fine-tuning (SFT) and reinforcement fine-tuning (RFT). Each paradigm presents a distinct trade-off: (…
GW-MoE: Resolving Uncertainty in MoE Router with Global Workspace Theory
Haoze Wu, Zihan Qiu, Zili Wang +2
Mixture-of-Experts (MoE) has been demonstrated as an efficient method to scale up models. By dynamically and sparsely selecting activated experts, MoE can effectively reduce comput…
Unlocking Continual Learning Abilities in Language Models
Wenyu Du, Shuang Cheng, Tongxu Luo +5
Language models (LMs) exhibit impressive performance and generalization capabilities. However, LMs struggle with the persistent challenge of catastrophic forgetting, which undermin…
HyperMoE: Towards Better Mixture of Experts via Transferring Among Experts
Hao Zhao, Zihan Qiu, Huijia Wu +3
The Mixture of Experts (MoE) for language models has been proven effective in augmenting the capacity of models by dynamically routing each input token to a specific subset of expe…
Heterogenous Memory Augmented Neural Networks
Zihan Qiu, Zhen Liu, Shuicheng Yan +2
It has been shown that semi-parametric methods, which combine standard neural networks with non-parametric components such as external memory modules and data retrieval, are partic…
Unlocking Emergent Modularity in Large Language Models
Zihan Qiu, Zeyu Huang, Jie Fu
Modular Neural Networks (MNNs) demonstrate various advantages over monolithic models. Existing MNNs are generally : their modular architectures are pre-defined,…