Large Language Models Can Self-Improve at Web Agent Tasks

Overview

This whitepaper examines how language models can improve their performance while navigating complex web environments. It focuses on agent workflows where multi-step interaction, limited training data, and changing interfaces make conventional supervised training difficult.

Why it matters

The paper frames self-improvement as a practical route to more capable web agents. By learning from interactions and feedback, an agent can refine action selection and build stronger task strategies over time. This research is relevant to teams developing autonomous systems for real digital workflows.