9 citations · 14 across the 13 of their papers we have counts for
5 papers · 1 filter
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…