Scaling Web Agent Training through Automatic Data Generation and Fine-grained Evaluation

• URL: http://arxiv.org/abs/2602.12544v1
• Date: Fri, 13 Feb 2026 02:52:18 GMT
• Title: Scaling Web Agent Training through Automatic Data Generation and Fine-grained Evaluation
• Authors: Lajanugen Logeswaran, Jaekyeom Kim, Sungryull Sohn, Creighton Glasscock, Honglak Lee,
• Abstract summary: We present a scalable pipeline for automatically generating high-quality training data for web agents.<n>We introduce a novel constraint-based evaluation framework that provides fine-grained assessment of progress towards task completion.
• Score: 54.945281159783896
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a scalable pipeline for automatically generating high-quality training data for web agents. In particular, a major challenge in identifying high-quality training instances is trajectory evaluation - quantifying how much progress was made towards task completion. We introduce a novel constraint-based evaluation framework that provides fine-grained assessment of progress towards task completion. This enables us to leverage partially successful trajectories, which significantly expands the amount of usable training data. We evaluate our method on a new benchmark we propose called BookingArena, which consists of complex booking tasks across 20 popular websites, and demonstrate that our distilled student model outperforms open-source approaches and matches or exceeds commercial systems, while being a significantly smaller model. Our work addresses the challenge of efficiently creating diverse, realistic web interaction datasets and provides a systematic evaluation methodology for complex structured web tasks.

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