Year: 2025
Type: conference-paper
Abstract: Agent self-improvement, where agents autonomously train their underlying Large Language Model (LLM) on self-sampled trajectories, shows promising results but often stagnates in web environments due to limited exploration and under-utilization of pretrained web knowledge.To improve the performance of self-improvement, we propose a novel framework that introduces a co-evolving World Model LLM.This world model predicts the next observation based on the current observation and action within the web environment.The World Model serves dual roles: (1) as a virtual web server generating self-instructed training data to continuously refine the agent's policy, and (2) as an imagination engine during inference, enabling look-ahead simulation to guide action selection for the agent LLM.Experiments in real-world web environments (Mind2Web-Live, WebVoyager, and GAIAweb) show a 10% performance gain over existing self-evolving agents, demonstrating the efficacy and generalizability of our approach, without using any distillation from more powerful close-sourced models 1 .
Institution: Tencent (China)
Language: English
Topic: Web Data Mining and Analysis
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4416036757