9 citations · 14 across the 13 of their papers we have counts for
13 papers
Hear, Invoke, and Understand: A Skill-Calling Multimodal Agent for Large Audio Language Models
Yuwen Wang, Tian-Hao Zhang, Minghao Cai +7
Complex acoustic problems may require models to perform acoustic operations, interact with external tools and reason over the resulting textual or processed-audio observations rath…
Labimus: A Simulation and Benchmark for Humanoid Dexterous Manipulation in Chemical Laboratory
Yuhan Wu, Zhao Jin, Tao Li +9
Laboratory automation has made remarkable progress through robotic platforms and AI-driven scientific reasoning. However, many laboratory operations (e.g., solid--solid transfer) r…
ArtiBench and ArtiBrain: Benchmarking Generalizable Vision-Language Articulated Object Manipulation
Yuhan Wu, Tiantian Wei, Shuo Wang +4
Interactive articulated manipulation requires long-horizon, multi-step interactions with appliances while maintaining physical consistency. Existing vision-language and diffusion-b…
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…
GIFT: Group-Relative Implicit Fine-Tuning Integrates GRPO with DPO and UNA
Zhichao Wang
This paper investigates whether reward matching is a viable alternative to reward maximization methods for on-policy RL of LLMs. Group-relative Implicit Fine-Tuning (GIFT) is propo…
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…
Optimal Exact Recovery in Semi-Supervised Learning: A Study of Spectral Methods and Graph Convolutional Networks
Hai-Xiao Wang, Zhichao Wang
We delve into the challenge of semi-supervised node classification on the Contextual Stochastic Block Model (CSBM) dataset. Here, nodes from the two-cluster Stochastic Block Model…
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…
UNA: A Unified Supervised Framework for Efficient LLM Alignment Across Feedback Types
Zhichao Wang, Bin Bi, Can Huang +7
RL alignment methods, including RLHF and DPO, are primarily based on pairwise preference data. Although scalar or score-based feedback has been collected in some settings, it is ra…
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.…
PAFT: A Parallel Training Paradigm for Effective LLM Fine-Tuning
Shiva Kumar Pentyala, Zhichao Wang, Bin Bi +6
Large language models (LLMs) have shown remarkable abilities in diverse natural language processing (NLP) tasks. The LLMs generally undergo supervised fine-tuning (SFT) followed by…