{
  "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": "2609.37539",
  "title": "SkillGym: Training Skill-Use Agents with Automatic Verifiable Environment Generation",
  "summary": "The method replaces manual curation of skill-use training data and ad hoc prompting with an automatic pipeline that builds executable, verifier-backed environments and uses the resulting verified trajectories for supervised finetuning. The cost is a crawl-filter-build-verify infrastructure (6.8k environments, 19k trajectories, builder-reviewer LLM calls) plus SFT compute, which exceeds a single 24 GB GPU budget at the paper's scale, though LoRA finetuning of a 7-9B model on a smaller trajectory set is feasible. Failure modes include verifier false positives contaminating SFT data, distribution mismatch between crawled skills and the reader's domain-specific skills, and gains that may not transfer if the production harness differs from the training environments.",
  "rationale": "The core insight (SFT on verified skill-use trajectories teaches reliable skill invocation) is directly relevant to production agents, but reproducing the full environment-generation pipeline requires infrastructure and data volume the reader does not have, so it warrants monitoring and small-scale adaptation rather than immediate adoption.",
  "technique": "Synthetic verified-trajectory SFT for skill-use agents",
  "family": "treino_eficiente",
  "recommendation": "observar",
  "published": "2026-09-29",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.37539",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 131,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces manual curation of skill-use training data and ad hoc prompting with an automatic pipeline that builds executable, verifier-backed environments and uses the resulting verified trajectories for supervised finetuning. The cost is a crawl-filter-build-verify infrastructure (6.8k environments, 19k trajectories, builder-reviewer LLM calls) plus SFT compute, which exceeds a single 24 GB GPU budget at the paper's scale, though LoRA finetuning of a 7-9B model on a smaller trajectory set is feasible. Failure modes include verifier false positives contaminating SFT data, distribution mismatch between crawled skills and the reader's domain-specific skills, and gains that may not transfer if the production harness differs from the training environments.",
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      "source_url": "https://arxiv.org/abs/2609.37539",
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    {
      "claim_id": "claim-02",
      "statement": "A Qwen3.5-9B model SFT-finetuned on 19k verified skill-use trajectories outperforms an untrained 397B model on two of four skill-use benchmarks, and raises relevant-skill reading rate from 28% to 96%.",
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      "source_url": "https://arxiv.org/abs/2609.37539",
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    }
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    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "security",
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      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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      "source_url": "",
      "source_page": null,
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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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}