5 citations · 5 across the 5 of their papers we have counts for
5 papers
UniPool: Learning Expert-to-Layer Ownership from Brief Global Access
Minbin Huang, Han Shi, Chuanyang Zheng +5
Most Mixture-of-Experts (MoE) transformers preassign each expert to one layer for the whole of training. We study whether a brief global-access phase can learn a compatible expert-…
Answer-Consistent Chain-of-thought Reinforcement Learning For Multi-modal Large Langauge Models
Minbin Huang, Runhui Huang, Chuanyang Zheng +4
Recent advances in large language models (LLMs) have demonstrated that reinforcement learning with verifiable rewards (RLVR) can significantly enhance reasoning abilities by direct…
Efficient Multi-modal Large Language Models via Visual Token Grouping
Minbin Huang, Runhui Huang, Han Shi +6
The development of Multi-modal Large Language Models (MLLMs) enhances Large Language Models (LLMs) with the ability to perceive data formats beyond text, significantly advancing a…
DialogGen: Multi-modal Interactive Dialogue System for Multi-turn Text-to-Image Generation
Minbin Huang, Yanxin Long, Xinchi Deng +6
Text-to-image (T2I) generation models have significantly advanced in recent years. However, effective interaction with these models is challenging for average users due to the need…
Getting More Juice Out of Your Data: Hard Pair Refinement Enhances Visual-Language Models Without Extra Data
Haonan Wang, Minbin Huang, Runhui Huang +7
Contrastive Language-Image Pre-training (CLIP) has become the standard for cross-modal image-text representation learning. Improving CLIP typically requires additional data and ret…