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Scalable behaviour cloning for browser agents via skill distillation from human interaction traces

A new arXiv preprint proposes converting human browser interaction trajectories into compact natural-language skills that agents can retrieve and compose, arguing that the bottleneck for browser agents is decision-making under incomplete information rather than low-level operations. The approach organizes distilled skills into a skill graph to enable consolidation rather than unbounded accumulation. The work positions collective human browsing behavior as a scalable, under-exploited source of reusable agent priors, potentially reducing reliance on manually designed task demonstrations.