{
  "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": "2610.19868",
  "title": "Explorer-Zero: Improving Self-Play for Long-Horizon Tasks with Decomposable Verifiable Rewards",
  "summary": "The method replaces human-annotated task datasets and binary-outcome verification with a learned proposer that generates tasks and records intermediate environment states as verifiable subtask checkpoints, trained jointly with a solver in alternating self-play RL. It costs an RL training pipeline with environment interaction, proposer-solver alternation, and multi-epoch rollouts, demonstrated at 30B scale, which exceeds a single 24 GB GPU and likely requires substantial API or cloud compute budget. It can fail if the proposer generates unachievable or degenerate tasks, if recorded state checkpoints misalign with true subtask completion, or if alternating training destabilizes the curriculum.",
  "rationale": "The gains are demonstrated only through RL fine-tuning of a 30B model with environment-in-the-loop rollouts, which is infeasible on the reader's constrained infrastructure, though the decomposable-verifiable-reward idea is worth tracking for future training capability.",
  "technique": "Self-play RL with decomposable verifiable subtask rewards",
  "family": "treino_eficiente",
  "recommendation": "observar",
  "published": "2026-10-14",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.19868",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces human-annotated task datasets and binary-outcome verification with a learned proposer that generates tasks and records intermediate environment states as verifiable subtask checkpoints, trained jointly with a solver in alternating self-play RL. It costs an RL training pipeline with environment interaction, proposer-solver alternation, and multi-epoch rollouts, demonstrated at 30B scale, which exceeds a single 24 GB GPU and likely requires substantial API or cloud compute budget. It can fail if the proposer generates unachievable or degenerate tasks, if recorded state checkpoints misalign with true subtask completion, or if alternating training destabilizes the curriculum.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.19868",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "30B agent reaches 59.67% task success on AppWorld via self-play, versus 62.65% for RL on human-annotated tasks; outperforms evaluated self-play baselines on ALFWorld, ScienceWorld, and AppWorld; self-play initialization improves subsequent human-data RL.",
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      "source_url": "https://arxiv.org/abs/2610.19868",
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      "source_excerpt": ""
    }
  ],
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      "dimension": "compute",
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    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "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": "",
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  "equations_source_sha256": "",
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}