{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A self-hosted model uses more compute or memory than your workload can justify.",
    "change": "Check for a usable pruned checkpoint and runtime support before attempting model surgery.",
    "baseline": "The original unpruned model at the same precision, context, and load.",
    "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": "Task quality, actual GPU memory, serving throughput, and preparation cost.",
    "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": "2610.25679",
  "title": "LOOP: Recovery-Aligned Iterative Layer Pruning via On-Policy Distillation",
  "summary": "Replaces one-shot layer pruning followed by a single recovery phase with an iterative loop that removes one layer at a time, runs short on-policy distillation after each removal, and reuses recovery signals to select subsequent layers. Costs repeated distillation cycles with on-policy trajectory generation (teacher rollouts at every shot), which raises compute and wall-clock budget well beyond a one-shot prune, though it preserves a dense architecture that runs on standard hardware after pruning. Can fail if the per-shot recovery budget is insufficient to make the pruning signal informative, if on-policy distillation is unstable for the target model, or if the accuracy gains over cheaper one-shot baselines do not justify the added training cost on a limited budget.",
  "rationale": "The method requires repeated on-policy distillation rounds per pruned layer, which is plausible but compute-intensive on a single 24 GB GPU, so its accuracy advantage should be validated against cheaper one-shot baselines before production commitment.",
  "technique": "Recovery-aligned iterative layer pruning with on-policy distillation",
  "family": "esparsidade_e_poda",
  "recommendation": "observar",
  "published": "2026-10-19",
  "as_of": "2026-10-25",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.25679",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "Replaces one-shot layer pruning followed by a single recovery phase with an iterative loop that removes one layer at a time, runs short on-policy distillation after each removal, and reuses recovery signals to select subsequent layers. Costs repeated distillation cycles with on-policy trajectory generation (teacher rollouts at every shot), which raises compute and wall-clock budget well beyond a one-shot prune, though it preserves a dense architecture that runs on standard hardware after pruning. Can fail if the per-shot recovery budget is insufficient to make the pruning signal informative, if on-policy distillation is unstable for the target model, or if the accuracy gains over cheaper one-shot baselines do not justify the added training cost on a limited budget.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.25679",
      "source_page": null,
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    },
    {
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      "statement": "LOOP consistently outperforms one-shot baselines in average accuracy across model scales and pruning ratios; no multiplicative factor is reported in the abstract.",
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      "source_url": "https://arxiv.org/abs/2610.25679",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
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    },
    {
      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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    },
    {
      "dimension": "security",
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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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      "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": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
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}