The Geometry of Learning to Avoid Interventions

תקציר מקורי באנגליתarXiv:2602.03825v2 Announce Type: replace Abstract: Human interventions are a common source of supervision in autonomous systems during deployment. Many existing approaches are based on avoiding interventions, yet the consequences of this objective are not well understood. We develop a geometric perspective on intervention learning that characterizes intervention avoidance as constraining policies to a face of the occupancy measure polytope. This view reveals that the effectiveness of intervention learning depends on the informativeness of the intervention strategy: highly informative interventions uniquely determine the solution, while weak interventions leave a large set of feasible policies, many of which are suboptimal. Motivated by this under-specification, we define Robust Interventi