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