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    "story_id": "mp-2026-10-02-015",
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    "headline": "Forward KL Kept More High-Value Actions in Play",
    "slug": "forward-kl-kept-more-high-value-actions-in-play",
    "dek": "FERPO improved policies without differentiating the critic with respect to actions.",
    "summary": "FERPO improved policies without differentiating the critic with respect to actions.",
    "body_text": "FERPO derives an entropy- and KL-regularized target action distribution from critic values, then fits the actor with forward KL and self-normalized importance sampling. MuJoCo Playground and ManiSkill tests showed competitive performance, sample-efficiency gains and faster actor updates than REPPO.",
    "why_it_matters": "FERPO improved policies without differentiating the critic with respect to actions.",
    "limitations": [],
    "importance": 8,
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    "tags": [
      "reinforcement learning",
      "continuous control",
      "policy optimization"
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    "corrections": []
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    {
      "source_id": "source-2026-10-02-017",
      "title": "arXiv preprint 2610.02198",
      "publisher": "arXiv",
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    "title": "Forward KL Kept More High-Value Actions in Play",
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