  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}.
