title: AutoWebGLM: Bootstrap And Reinforce A Large Language Model-based Web
    Navigating Agent
summary: 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 https://github.com/THUDM/AutoWebGLM.
paper_authors._id: 660f6455dcdd0ff2df12f559
paper_authors.name: Hanyu Lai
paper_authors._id: 660f6455dcdd0ff2df12f55a
paper_authors.name: Xiao Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-05T16:12:35.192Z
paper_authors._id: 62f098602ca4d32a7cd87aba
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1659934844914-62f098602ca4d32a7cd87aba.jpeg
paper_authors.fullname: Xiao Liu
paper_authors.type: user
paper_authors.user: ShawLiu
paper_authors._id: 660f6455dcdd0ff2df12f55b
paper_authors.name: Iat Long Iong
paper_authors._id: 660f6455dcdd0ff2df12f55c
paper_authors.name: Shuntian Yao
paper_authors._id: 660f6455dcdd0ff2df12f55d
paper_authors.name: Yuxuan Chen
paper_authors._id: 660f6455dcdd0ff2df12f55e
paper_authors.name: Pengbo Shen
paper_authors._id: 660f6455dcdd0ff2df12f55f
paper_authors.name: Hao Yu
paper_authors._id: 660f6455dcdd0ff2df12f560
paper_authors.name: Hanchen Zhang
paper_authors._id: 660f6455dcdd0ff2df12f561
paper_authors.name: Xiaohan Zhang
paper_authors._id: 660f6455dcdd0ff2df12f562
paper_authors.name: Yuxiao Dong
paper_authors._id: 660f6455dcdd0ff2df12f563
paper_authors.name: Jie Tang
paper_title: AutoWebGLM: Bootstrap And Reinforce A Large Language Model-based Web
    Navigating Agent
paper_summary: 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 https://github.com/THUDM/AutoWebGLM.
paper_ai_summary: AutoWebGLM, an automated web navigation agent based on ChatGLM3-6B, addresses webpage complexity and action versatility through HTML simplification and reinforcement learning, outperforming GPT-4 in real-world web navigation tasks.
paper_ai_keywords: LLMs GPT-4 ChatGLM3-6B HTML simplification reinforcement learning rejection sampling curriculum training bilingual benchmark AutoWebBench