GTA: Generating Long-Horizon Tasks for Web Agents at Scale
Web agents, which couple language models with browsing and tool-use capabilities, show promise as open web assistants. Yet progress is increasingly limited by the lack of scalable, process-level supervision. Existing benchmarks are largely manually constructed, providing only coarse start-goal annotations without intermediate trajectories, while recent automatic generation efforts remain expensive, biased, and shallow. These limitations prevent reliable training and evaluation of agents that must generalize to realistic, multi-hop, cross-page tasks. We introduce a scalable framework, GTA, that integrates crawling, retrieval-based seeding, in-context generation, and automated quality contr...
| Authors | Tenghao Huang, Kung-Hsiang Huang, Prafulla Kumar Choubey et al. |
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| Primary category | cs.AI |
| Published | 28 May 2026 |
| Last updated at source | 28 May 2026 |
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