143 citations · 146 across the 14 of their papers we have counts for
10 papers · 1 filter
More Than Seeing: Vision as Scalable Anchors in Native Multimodal Pre-Training
Haoyuan Wu, Aoqi Wu, Jiani Hu +1
Native multimodal pre-training trains a single model from scratch on text and multimodal data, enabling it to understand both language and visual inputs. However, how visual signal…
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)…
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…
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…
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…
Efficient OpAmp Adaptation for Zoom Attention to Golden Contexts
Haoyuan Wu, Rui Ming, Haisheng Zheng +2
Large language models (LLMs) have shown significant promise in question-answering (QA) tasks, particularly in retrieval-augmented generation (RAG) scenarios and long-context applic…
Divergent Thoughts toward One Goal: LLM-based Multi-Agent Collaboration System for Electronic Design Automation
Haoyuan Wu, Haisheng Zheng, Zhuolun He +1
Recently, with the development of tool-calling capabilities in large language models (LLMs), these models have demonstrated significant potential for automating electronic design a…
Customized Retrieval Augmented Generation and Benchmarking for EDA Tool Documentation QA
Yuan Pu, Zhuolun He, Tairu Qiu +2
Retrieval augmented generation (RAG) enhances the accuracy and reliability of generative AI models by sourcing factual information from external databases, which is extensively emp…