title: InSTA: Towards Internet-Scale Training For Agents
summary: 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.
paper_authors._id: 67aae05bb893603a0b4b241d
paper_authors.name: Brandon Trabucco
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-20T17:37:50.173Z
paper_authors._id: 632d8b2e1d8a018adf4f98f1
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/632d8b2e1d8a018adf4f98f1/vGvpkxyGLNQSJmEONR2uX.jpeg
paper_authors.fullname: Brandon Trabucco
paper_authors.type: user
paper_authors.user: btrabucco
paper_authors._id: 67aae05bb893603a0b4b241e
paper_authors.name: Gunnar Sigurdsson
paper_authors._id: 67aae05bb893603a0b4b241f
paper_authors.name: Robinson Piramuthu
paper_authors._id: 67aae05bb893603a0b4b2420
paper_authors.name: Ruslan Salakhutdinov
paper_title: InSTA: Towards Internet-Scale Training For Agents
paper_summary: 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.
paper_ai_summary: A pipeline using LLMs generates tasks, completes trajectories, and judges success for web navigation agents, improving performance and generalization compared to human demonstrations.
paper_ai_keywords: LLM latent diffusion models task generation trajectory completion trajectory judgment Llama 3.1 70B human demonstrations Step Accuracy generalization Mind2Web WebLINX