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

arXiv · AI, language, vision and robotics · article · Oct 8, 2026 · UTC

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-intervention filters are myopic to long-horizon task return because they minimize only instantaneous action deviation. Hard projections are also undefined when no safe action exists. We present FAITH, a feasibility-aware, model-free framework that approximates the optimal state-action safety value and am

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