Scalable Behaviour Cloning on Browser Using via Skill Distillation

Browser demonstrations encode procedural knowledge about where to navigate, which information to preserve, and how to verify task completion. Reusing this knowledge requires separating the procedure from the incidental details of a recorded session. We present BrowserBC, a framework that distills browser trajectories into structured natural-language skills and organizes them in a skill graph. Each skill captures applicability conditions, execution steps, progress checks, completion evidence, and recovery guidance. The executor interprets these instructions against the live page, enabling skills to be produced and consumed by different models. Evaluations on WebArena-Hard and ClawBench show improved task completion under task-family and rubric-informed, case-matched guidance, respectively, with shorter interactions on WebArena-Hard. Cross-model experiments demonstrate skill reuse across executors, while real-world scenario tests show benefits from greater skill availability and graph organization in both success and token efficiency. Desktop cases illustrate how procedural guidance supports navigation and completion checks while revealing persistent execution difficulties. Together, these findings support skill distillation as a way to accumulate and share procedural knowledge across agents.