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README.md

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 **(1) Carnegie Mellon University, Machine Learning Department    (2) Amazon**
 
-The predominant approach for training web navigation agents gathers human demonstrations for a set of popular websites and hand-written tasks, but it is becoming clear that human data are an inefficient resource. We develop a pipeline to facilitate Internet-scale training for agents without laborious human annotations. In the first stage, an LLM generates tasks for 150k diverse websites. In the next stage, LLM agents complete tasks and produce trajectories. In the final stage, an LLM reviews the trajectories and judges their success. Language models are competitive with human annotators, detecting and filtering out harmful content with an accuracy of 97%, generating feasible tasks with an 89% rate, and judging successful trajectories with an 82.6% accuracy. Scaling the pipeline, agents based on Llama 3.1 70B solve 16.7% of tasks for 150k sites. Training on the data generated by our pipeline is competitive with training on human demonstrations. In data-limited settings derived from Mind2Web and WebLINX, we improve Step Accuracy by up to +89.5% and +122.1% respectively for agents trained on mixtures of data from our pipeline, and human data. When training agents with all available human data from these benchmarks, agents fail to generalize to diverse real sites, and adding our data improves their generalization by +149.0% for WebLINX and +156.3% for Mind2Web. Code available at: [data-for-agents.github.io](https://data-for-agents.github.io).
+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: [data-for-agents.github.io](https://data-for-agents.github.io).
 
-[website](https://data-for-agents.github.io)    |    [paper](https://arxiv.org/abs/2502.06776)    |    [code](https://github.com/data-for-agents/insta)    |    [data](https://huggingface.co/datasets/data-for-agents/insta-150k-v2)
+[website](https://data-for-agents.github.io)    |    [paper](https://arxiv.org/abs/2502.06776)    |    [data](https://huggingface.co/datasets/data-for-agents/insta-150k-v3)
 
 ## Section 4 - Internet-Scale Task Generation