5 papers · 1 filter
Sparse-BitNet: 1.58-bit LLMs are Naturally Friendly to Semi-Structured Sparsity
Di Zhang, Xun Wu, Shaohan Huang +9
Semi-structured N:M sparsity and low-bit quantization (e.g., 1.58-bit BitNet) are two promising approaches for improving the efficiency of large language models (LLMs), yet they ha…
Black-Box On-Policy Distillation of Large Language Models
Tianzhu Ye, Li Dong, Zewen Chi +3
Black-box distillation creates student large language models (LLMs) by learning from a proprietary teacher model's text outputs alone, without access to its internal logits or para…
FlashBack:Efficient Retrieval-Augmented Language Modeling for Long Context Inference
Runheng Liu, Xingchen Xiao, Heyan Huang +2
Retrieval-Augmented Language Modeling (RALM) by integrating large language models (LLM) with relevant documents from an external corpus is a proven method for enabling the LLM to g…
Think Only When You Need with Large Hybrid-Reasoning Models
Lingjie Jiang, Xun Wu, Shaohan Huang +7
Recent Large Reasoning Models (LRMs) have shown substantially improved reasoning capabilities over traditional Large Language Models (LLMs) by incorporating extended thinking proce…
Reward Reasoning Model
Jiaxin Guo, Zewen Chi, Li Dong +4
Reward models play a critical role in guiding large language models toward outputs that align with human expectations. However, an open challenge remains in effectively utilizing t…