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-evolu...
| Authors | Hyungjoo Chae, Jungsoo Park, Alan Ritter |
|---|---|
| Primary category | cs.CL |
| Published | 11 March 2026 |
| Last updated at source | 11 March 2026 |
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