Enhancing Web Agents with Explicit Rollback Mechanisms

• URL: http://arxiv.org/abs/2504.11788v1
• Date: Wed, 16 Apr 2025 05:41:20 GMT
• Title: Enhancing Web Agents with Explicit Rollback Mechanisms
• Authors: Zhisong Zhang, Tianqing Fang, Kaixin Ma, Wenhao Yu, Hongming Zhang, Haitao Mi, Dong Yu,
• Abstract summary: We enhance web agents with an explicit rollback mechanism, enabling the agent to revert back to a previous state in its navigation trajectory.<n>This mechanism gives the model the flexibility to directly control the search process, leading to an effective and efficient web navigation method.
• Score: 55.276852838877346
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With recent advancements in large language models, web agents have been greatly improved. However, dealing with complex and dynamic web environments requires more advanced planning and search abilities. Previous studies usually adopt a greedy one-way search strategy, which may struggle to recover from erroneous states. In this work, we enhance web agents with an explicit rollback mechanism, enabling the agent to revert back to a previous state in its navigation trajectory. This mechanism gives the model the flexibility to directly control the search process, leading to an effective and efficient web navigation method. We conduct experiments on two live web navigation benchmarks with zero-shot and fine-tuning settings. The results demonstrate the effectiveness of our proposed approach.

Related papers

• R2D2: Remembering, Reflecting and Dynamic Decision Making for Web Agents [53.94879482534949]
  Current models often struggle with efficient navigation and action execution due to limited visibility and understanding of web structures.<n>Our proposed R2D2 framework addresses these challenges by integrating two paradigms: Remember and Reflect.<n>Our findings suggest that a combination of memory-enhanced navigation and reflective learning promisingly advances the capabilities of web agents.
  arXiv  Detail & Related papers  (2025-01-21T20:21:58Z)
• WEPO: Web Element Preference Optimization for LLM-based Web Navigation [3.9400326648635566]
  This paper introduces a novel approach to web navigation tasks, called Web Element Preference Optimization (WEPO)<n>We utilize unsupervised preference learning by sampling distance-based non-salient web elements as negative samples, optimizing maximum likelihood objective within Direct Preference Optimization (DPO)<n>The results show that our method achieved the state-of-the-art, with an improvement of 13.8% over WebAgent and 5.3% over the visual language model CogAgent baseline.
  arXiv  Detail & Related papers  (2024-12-14T08:25:28Z)
• Auto-Intent: Automated Intent Discovery and Self-Exploration for Large Language Model Web Agents [68.22496852535937]
  We introduce Auto-Intent, a method to adapt a pre-trained large language model (LLM) as an agent for a target domain without direct fine-tuning. Our approach first discovers the underlying intents from target domain demonstrations unsupervisedly. We train our intent predictor to predict the next intent given the agent's past observations and actions.
  arXiv  Detail & Related papers  (2024-10-29T21:37:04Z)
• OpenWebVoyager: Building Multimodal Web Agents via Iterative Real-World Exploration, Feedback and Optimization [66.22117723598872]
  We introduce an open-source framework designed to facilitate the development of multimodal web agent. We first train the base model with imitation learning to gain the basic abilities. We then let the agent explore the open web and collect feedback on its trajectories.
  arXiv  Detail & Related papers  (2024-10-25T15:01:27Z)
• Tree Search for Language Model Agents [69.43007235771383]
  We propose an inference-time search algorithm for LM agents to perform exploration and multi-step planning in interactive web environments. Our approach is a form of best-first tree search that operates within the actual environment space. It is the first tree search algorithm for LM agents that shows effectiveness on realistic web tasks.
  arXiv  Detail & Related papers  (2024-07-01T17:07:55Z)
• Diffusion-Reinforcement Learning Hierarchical Motion Planning in Adversarial Multi-agent Games [6.532258098619471]
  We focus on a motion planning task for an evasive target in a partially observable multi-agent adversarial pursuit-evasion games (PEG) These pursuit-evasion problems are relevant to various applications, such as search and rescue operations and surveillance robots. We propose a hierarchical architecture that integrates a high-level diffusion model to plan global paths responsive to environment data.
  arXiv  Detail & Related papers  (2024-03-16T03:53:55Z)
• LASER: LLM Agent with State-Space Exploration for Web Navigation [57.802977310392755]
  Large language models (LLMs) have been successfully adapted for interactive decision-making tasks like web navigation. Previous methods implicitly assume a forward-only execution mode for the model, where they only provide oracle trajectories as in-context examples. We propose to model the interactive task as state space exploration, where the LLM agent transitions among a pre-defined set of states by performing actions to complete the task.
  arXiv  Detail & Related papers  (2023-09-15T05:44:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.