arXiv:2605.20291v1 Announce Type: new Abstract: Large language models (LLMs) have enabled web agents that follow natural language goals through multi-step browser interactions. However, agents fine-tuned on specific trajectories and domain often struggle to generalize out of domain, and offline training can be compute-inefficient due to noisy, redundant trajectories and long accessibility-tree (AXTree) states. To address both issues, we propose Weasel, a trajectory selection method for offline training of web agents. Weasel selects a fixed-budget subset of trajectory steps by optimizing an obj
The proliferation of LLMs is accelerating research into their robust application, making out-of-domain generalization a critical bottleneck for deploying web agents effectively.
Improving generalization for web agents allows more reliable and versatile automation, fundamentally expanding the scope and impact of AI in digital workflows.
The ability to train web agents more efficiently and robustly on diverse data means they can operate across a wider range of unfamiliar web environments without extensive re-training.
- · AI software developers
- · Automation platforms
- · Businesses leveraging AI agents
- · Cloud computing providers
- · Tasks requiring manual web navigation
- · Legacy automation software
More capable and reliable web agents are developed and deployed.
Increased automation of online tasks and B2B workflow integration, reducing human intervention.
Accelerated development of general-purpose AI agents capable of operating across heterogeneous digital environments.
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Read at arXiv cs.LG