Training Needs Trustworthy Worlds: Verified Synthetic Web Environments for Agent Learning
The paper addresses flaws in current synthetic web environments by proposing a framework to verify and repair generated scaffolds before training AI agents. It creates executable, auditable environments where agents learn from reliable supervision, ensuring policy training is grounded in reality rather than defects.