Navigating WebAI: Training Agents to Complete Web Tasks with Large Language Models and Reinforcement Learning

arXiv:2405.00516v1 Announce Type: new
Abstract: Recent advancements in language models have demonstrated remarkable improvements in various natural language processing (NLP) tasks such as web navigation. Supervised learning (SL) approaches have achieved impressive performance while utilizing significantly less training data compared to previous methods. However, these SL-based models fall short when compared to reinforcement learning (RL) approaches, which have shown superior results. In this paper, we propose a novel approach that combines SL and RL techniques over the MiniWoB benchmark to …

abstract agents arxiv cs.ai cs.cl cs.lg data however improvements language language models language processing large language large language models natural natural language natural language processing navigation nlp performance processing reinforcement reinforcement learning supervised learning tasks training training data type web web navigation while