10 papers
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.…
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…
One-Token Rollout: Guiding Supervised Fine-Tuning of LLMs with Policy Gradient
Rui Ming, Haoyuan Wu, Shoubo Hu +2
Supervised fine-tuning (SFT) is the predominant method for adapting large language models (LLMs), yet it often struggles with generalization compared to reinforcement learning (RL)…
ToTRL: Unlock LLM Tree-of-Thoughts Reasoning Potential through Puzzles Solving
Haoyuan Wu, Xueyi Chen, Rui Ming +4
Large language models (LLMs) demonstrate significant reasoning capabilities, particularly through long chain-of-thought (CoT) processes, which can be elicited by reinforcement lear…
On-Policy Optimization with Group Equivalent Preference for Multi-Programming Language Understanding
Haoyuan Wu, Rui Ming, Jilong Gao +6
Large language models (LLMs) achieve remarkable performance in code generation tasks. However, a significant performance disparity persists between popular programming languages (e…
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…