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GTA: Generating Long-Horizon Tasks for Web Agents at Scale

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arXiv:2605. 29218v1 Announce Type: new Abstract: Web agents, which couple language models with browsing and tool-use capabilities, show promise as open web assistants.

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Authors: Tenghao Huang, Kung-Hsiang Huang, Prafulla Kumar Choubey, Yilun Zhou, Muhao Chen, Jonathan May, Chien-Sheng Wu
Released: 2026-05-29 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because GTA: Generating Long-Horizon Tasks for Web Agents at Scale overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: text, eval, assistant, line, rate, control, without, position. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2605.29218

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