{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "You already need task-specific training and want to reduce its memory or compute cost.",
    "change": "Change only the training technique on a small controlled run after checking prerequisites.",
    "baseline": "Your current training recipe with the same model, data split, and evaluation.",
    "steps": [
      "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.",
      "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.",
      "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.",
      "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test."
    ],
    "measure": "Held-out quality, peak memory, training time, and full compute cost to the target quality.",
    "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation."
  },
  "builder_review": null,
  "arxiv_id": "2609.30226",
  "title": "PoEM: Predicting RL Outcomes from Existing Policies",
  "summary": "PoEM replaces re-running RL post-training whenever the reward changes by approximating the target policy as a weighted combination in log-space of policies already trained on other rewards, with weights estimated from reward or basis-policy outputs on samples. Its cost is storage and inference over multiple post-trained models plus the estimation step, and it presumes a library of existing post-trained policies that a small team may not possess. It can fail when the new reward lies outside the approximately low-rank subspace spanned by the existing log-policies, producing a poor approximation of the true RL outcome.",
  "rationale": "The method avoids RL retraining, which suits constrained compute, but it requires multiple already post-trained policies and its accuracy depends on an empirical low-rank assumption, so workload-specific validation is necessary before production adoption.",
  "technique": "Predicting RL policies as log-linear combinations of existing policies",
  "family": "treino_eficiente",
  "recommendation": "testar",
  "published": "2026-09-24",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.30226",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 1,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "PoEM replaces re-running RL post-training whenever the reward changes by approximating the target policy as a weighted combination in log-space of policies already trained on other rewards, with weights estimated from reward or basis-policy outputs on samples. Its cost is storage and inference over multiple post-trained models plus the estimation step, and it presumes a library of existing post-trained policies that a small team may not possess. It can fail when the new reward lies outside the approximately low-rank subspace spanned by the existing log-policies, producing a poor approximation of the true RL outcome.",
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      "result": "",
      "baseline": "",
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      "source_url": "https://arxiv.org/abs/2609.30226",
      "source_page": null,
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    },
    {
      "claim_id": "claim-02",
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      "source_url": "https://arxiv.org/abs/2609.30226",
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    }
  ],
  "exposure_map": [
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      "dimension": "latency",
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      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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    },
    {
      "dimension": "security",
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    },
    {
      "dimension": "data_and_training",
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    },
    {
      "dimension": "reproducibility",
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      "source_page": null,
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    }
  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
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}