5 papers
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…
A Deep Neural Network Based Approach to Building Budget-Constrained Models for Big Data Analysis
Rui Ming, Haiping Xu, Shannon E. Gibbs +2
Deep learning approaches require collection of data on many different input features or variables for accurate model training and prediction. Since data collection on input feature…