Autowebglm: Bootstrap And Reinforce A Large Language Model-based Web Navigating Agent

Lai Hanyu, Liu Xiao, Iong Iat Long, Yao Shuntian, Chen Yuxuan, Shen Pengbo, Yu Hao, Zhang Hanchen, Zhang Xiaohan, Dong Yuxiao, Tang Jie. Arxiv 2024

[Paper] [Code]    
Agentic GPT Has Code Model Architecture Reinforcement Learning Training Techniques

Large language models (LLMs) have fueled many intelligent agent tasks, such as web navigation – but most existing agents perform far from satisfying in real-world webpages due to three factors: (1) the versatility of actions on webpages, (2) HTML text exceeding model processing capacity, and (3) the complexity of decision-making due to the open-domain nature of web. In light of the challenge, we develop AutoWebGLM, a GPT-4-outperforming automated web navigation agent built upon ChatGLM3-6B. Inspired by human browsing patterns, we design an HTML simplification algorithm to represent webpages, preserving vital information succinctly. We employ a hybrid human-AI method to build web browsing data for curriculum training. Then, we bootstrap the model by reinforcement learning and rejection sampling to further facilitate webpage comprehension, browser operations, and efficient task decomposition by itself. For testing, we establish a bilingual benchmark – AutoWebBench – for real-world web browsing tasks. We evaluate AutoWebGLM across diverse web navigation benchmarks, revealing its improvements but also underlying challenges to tackle real environments. Related code, model, and data will be released at \url{https://github.com/THUDM/AutoWebGLM}.

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