Stephen Luo Sheng

Existing robotic foundation policies are trained primarily via large-scale imitation learning. While such models demonstrate strong capabilities, they often struggle with long-horizon tasks due to distribution shift and error accumulation.While reinforcement learning (RL) can finetune these models, it cannot work well across diverse tasks without manual reward engineering.

Retrieval and ranking lie at the heart of several applications like search, question-answering, and recommendations. The use of Large language models (LLMs) such as BERT in these applications have shown promising results in recent times. Recent works on text-based retrievers and rankers show promising results by using bi-encoders (BE) architecture with BERT like LLMs for retrieval and a cross-attention transformer (CAT) architecture BERT or other LLMs for ranking the results retrieved.

Open AccessArticle by Stephen Luo Sheng Yong 1, Jing Lin Ng 1,*, Yuk Feng Huang 2 and Chun Kit Ang 3 1 Department of Civil Engineering, Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, Malaysia 2 Department of Civil Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Petaling Jaya 43000, Malaysia 3 Department of Mechanical Engineering, Faculty of Engineering, Technology and Built Environment, UCSI...