Safe and Scalable Web Agent Learning via Recreated Websites
Training autonomous web agents is fundamentally limited by the environments they learn from: real-world websites are unsafe to explore, hard to reset, and rarely provide verifiable feedback. We propose VeriEnv, a framework that treats language models as environment creators, automatically cloning real-world websites into fully executable, verifiable synthetic environments. By exposing controlled internal access via a Python SDK, VeriEnv enables agents to self-generate tasks with deterministic, programmatically verifiable rewards, eliminating reliance on heuristic or LLM-based judges. This design decouples agent learning from unsafe real-world interaction while enabling scalable self-evolution through environment expansion. Through experiments on web agent benchmarks, we show that agents trained with VeriEnv generalize to unseen websites, achieve site-specific mastery through self-evolving training, and benefit from scaling the number of training environments. Code and resources will be released at https://github.com/kyle8581/VeriEnv upon acceptance.
69b82de56b3f36c249fbc2fd
Hyungjoo Chae
admin_assigned
2026-03-17T08:41:40.545Z
64c8f4cec547ed5243ebd0a8
https://cdn-avatars.huggingface.co/v1/production/uploads/64c8f4cec547ed5243ebd0a8/MiOH5YbMg8Gh9KYlQsLmX.jpeg
Hyungjoo Chae
hyungjoochae
user
hyungjoochae
69b82de56b3f36c249fbc2fe
Jungsoo Park
69b82de56b3f36c249fbc2ff
Alan Ritter
admin_assigned
2026-03-17T08:41:50.783Z
6377da9065ce6b04bd493cf8
/avatars/6be2632d6da49857928f8bcce8278f49.svg
Alan Ritter
rittera
user
rittera
Safe and Scalable Web Agent Learning via Recreated Websites
Training autonomous web agents is fundamentally limited by the environments they learn from: real-world websites are unsafe to explore, hard to reset, and rarely provide verifiable feedback. We propose VeriEnv, a framework that treats language models as environment creators, automatically cloning real-world websites into fully executable, verifiable synthetic environments. By exposing controlled internal access via a Python SDK, VeriEnv enables agents to self-generate tasks with deterministic, programmatically verifiable rewards, eliminating reliance on heuristic or LLM-based judges. This design decouples agent learning from unsafe real-world interaction while enabling scalable self-evolution through environment expansion. Through experiments on web agent benchmarks, we show that agents trained with VeriEnv generalize to unseen websites, achieve site-specific mastery through self-evolving training, and benefit from scaling the number of training environments. Code and resources will be released at https://github.com/kyle8581/VeriEnv upon acceptance.
VeriEnv enables safe and scalable training of web agents by creating synthetic, verifiable environments from real websites through language model-based cloning.
language models environment creators synthetic environments verifiable rewards web agent benchmarks self-evolving training