{
  "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": "2610.06387",
  "title": "Scaling Down the Scaling Laws: Parameter Efficiency and Compute-Optimal Training in Resource-Constrained Large Language Models",
  "summary": "This is a survey, not a deployable method: it synthesizes scaling laws, compute-optimal training (Chinchilla-style token-parameter allocation), data pruning, quantization, and low-rank adaptation as replacements for naive scale maximization. It costs nothing to consult but provides no new technique, and its explicit caveat is that scaling principles derived on enterprise infrastructure may not generalize to small models and constrained hardware. The reader risks misapplying compute-optimal ratios or efficiency heuristics that were validated at scales far beyond a single 24 GB GPU.",
  "rationale": "As a review it is useful background for prioritizing efficient-training techniques, but it offers no validated, ready-to-adopt method and itself warns that its conclusions may not transfer to resource-constrained settings.",
  "technique": "Compute-optimal training and parameter-efficient scaling review",
  "family": "treino_eficiente",
  "recommendation": "observar",
  "published": "2026-10-05",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.06387",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
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      "claim_id": "claim-01",
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    }
  ],
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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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  "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": "",
  "exposures_reviewed_at": "",
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}