FAITH: Feasibility-Aware Safety-Filtered RL for High-Dimensional Systems

Safe reinforcement learning commonly places safety and task performance in the same policy objective, where they can introduce competing updates. Safety filters separate them at action execution, but classical designs require an analytic safety function and dynamics model, and standard minimal-inter