143 citations · 146 across the 11 of their papers we have counts for
6 papers · 1 filter
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)…
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…