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    "headline": "The Safety Filter Learned Which Constraints Were Reachable",
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    "dek": "FAITH adds feasibility awareness to safety-filtered reinforcement learning for high-dimensional systems.",
    "summary": "FAITH adds feasibility awareness to safety-filtered reinforcement learning for high-dimensional systems.",
    "body_text": "A safety filter can demand an action that the controlled system cannot physically realize. FAITH accounts for that feasibility boundary while training and filtering, aiming to avoid both unsafe motion and impossible corrections. The results concern simulated high-dimensional control tasks and should not be read as certification for deployed machinery.",
    "why_it_matters": "FAITH adds feasibility awareness to safety-filtered reinforcement learning for high-dimensional systems.",
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      "A safety filter can demand an action that the controlled system cannot physically realize.",
      "The results concern simulated high-dimensional control tasks and should not be read as certification for deployed machinery."
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        "qualification": "A safety filter can demand an action that the controlled system cannot physically realize."
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    "tags": [
      "safe reinforcement learning",
      "feasibility"
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      "title": "arXiv preprint 2610.12432, original v1",
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