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cs.CL2026
Scaling Native Multimodal Pre-Training From Scratch
Haoyuan Wu, Aoqi Wu, Hai Wang +3
Although large language models (LLMs) exhibit remarkable reasoning capabilities, their reliance on text-only pre-training restricts the perception of the multimodal physical world.…
cs.CL2026
Diversity or Precision? A Deep Dive into Next Token Prediction
Haoyuan Wu, Hai Wang, Jiajia Wu +5
Recent advancements have shown that reinforcement learning (RL) can substantially improve the reasoning abilities of large language models (LLMs). The effectiveness of such RL trai…
cs.CL2025
Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts
Haoyuan Wu, Haoxing Chen, Xiaodong Chen +10
The Mixture of Experts (MoE) architecture is a cornerstone of modern state-of-the-art (SOTA) large language models (LLMs). MoE models facilitate scalability by enabling sparse para…