InSTA: Towards Internet-Scale Training For Agents

TL;DR

This paper presents a scalable pipeline for training web navigation agents using large language models to annotate, execute, and filter tasks across the internet, reducing reliance on human data and achieving competitive performance.

Contribution

The authors introduce a novel internet-scale training pipeline that leverages LLMs for annotation, execution, and filtering, enabling efficient training of web agents without human supervision.

Findings

Achieved a success rate of 56.9% with the top agent.

LLM-based filtering achieves 97% accuracy in identifying harmful content.

Agents trained with this pipeline outperform larger models in web navigation tasks.

Abstract

The predominant approach for training web navigation agents is to gather human demonstrations for a set of popular websites and hand-written tasks, but it is becoming clear that human data is an inefficient resource. We develop a pipeline to facilitate internet-scale training for agents without laborious human annotations. In the first stage, an LLM annotates 150k sites with agentic tasks. In the next stage, LLM agents complete tasks and produce trajectories. In the final stage, an LLM filters trajectories by judging their success. Language models are powerful data curation tools, identifying harmful content with an accuracy of 97%, judging successful trajectories with an accuracy of 82.6%, and producing effective data. We train agents based on Qwen 3 1.7B that are competitive with frontier LLMs as web agents, while being smaller and faster. Our top agent reaches a success rate of 56.9%, outperforming the data collection policy Qwen 3 235B, a 235 times larger Llama 4 Maverick, and reaching 94.7% of the performance of Gemini 2.5 Flash. We are releasing code, models and data at: https://data-for-agents.github.io.