4 citations · 4 across the 7 of their papers we have counts for
7 papers · 1 filter
RLSR: Reinforcement Learning with Supervised Reward Outperforms SFT in Instruction Following
Zhichao Wang, Andy Wong, Ruslan Belkin
After the pretraining stage of LLMs, techniques such as SFT, RLHF, RLVR, and RFT are applied to enhance instruction-following ability, mitigate undesired responses, improve reasoni…
Review of Inference-Time Scaling Strategies: Reasoning, Search and RAG
Zhichao Wang, Cheng Wan, Dong Nie
The performance gains of LLMs have historically been driven by scaling up model size and training data. However, the rapidly diminishing availability of high-quality training data…
Diversity Enhances an LLM's Performance in RAG and Long-context Task
Zhichao Wang, Bin Bi, Yanqi Luo +2
The rapid advancements in large language models (LLMs) have highlighted the challenge of context window limitations, primarily due to the quadratic time complexity of the self-atte…
Rate, Explain and Cite (REC): Enhanced Explanation and Attribution in Automatic Evaluation by Large Language Models
Aliyah R. Hsu, James Zhu, Zhichao Wang +11
LLMs have demonstrated impressive proficiency in generating coherent and high-quality text, making them valuable across a range of text-generation tasks. However, rigorous evaluati…
UFT: Unifying Fine-Tuning of SFT and RLHF/DPO/UNA through a Generalized Implicit Reward Function
Zhichao Wang, Bin Bi, Zixu Zhu +6
By pretraining on trillions of tokens, an LLM gains the capability of text generation. However, to enhance its utility and reduce potential harm, SFT and alignment are applied sequ…
Reinforcement Learning for LLM Post-Training: A Survey
Zhichao Wang, Kiran Ramnath, Bin Bi +9
Large language models (LLMs) trained via pretraining and supervised fine-tuning (SFT) can still produce harmful and misaligned outputs, or struggle in domains like math and coding.…