3 papers
cs.AI2026
Offline-Online Curriculum RL for Multimodal Reasoning
Wendi Deng, Hang Du, Guoshun Nan +11
Multimodal large language models exhibit capabilities on reasoning tasks, yet often produce flawed intermediate steps while yielding correct final answers. This behavior undermines…
cs.LG2025
Two Is Better Than One: Rotations Scale LoRAs
Hongcan Guo, Guoshun Nan, Yuan Yang +9
Scaling Low-Rank Adaptation (LoRA)-based Mixture-of-Experts (MoE) facilitates large language models (LLMs) to efficiently adapt to diverse tasks. However, traditional gating mechan…
cs.CL2025
Advancing Expert Specialization for Better MoE
Hongcan Guo, Haolang Lu, Guoshun Nan +8
Mixture-of-Experts (MoE) models enable efficient scaling of large language models (LLMs) by activating only a subset of experts per input. However, we observe that the commonly use…