{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A narrow, repeated product task may not need your largest model.",
    "change": "Check whether a released student model fits the task before generating training data.",
    "baseline": "Your current model and an off-the-shelf small model on the same held-out tasks.",
    "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": "Student quality, serving cost, teacher calls, training cost, and failures on unusual inputs.",
    "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": "2610.19439",
  "title": "Progress-Aware Distillation for Mitigating Stagnation in Small Language Model Agents",
  "summary": "ProD replaces plain supervised fine-tuning on teacher trajectories with an iterative distillation procedure that detects stagnation in student trajectories and adds an adaptive margin penalty to discourage unproductive loops. Costs include access to a teacher model for trajectory comparison, stagnation detection and margin computation during training, and iterative training rounds; on a single 24 GB GPU this is feasible for 0.6B-8B students with parameter-efficient fine-tuning but adds training complexity and time. It can fail if teacher trajectories are unavailable for the target domain, if the stagnation detector misfires and penalizes legitimate exploration, or if the student is too small to absorb the corrected behavior.",
  "rationale": "The method targets a concrete failure mode of distilled small agents and fits constrained infrastructure for students up to 8B, but it requires teacher data and iterative training whose benefit must be validated on the reader's own agentic workload.",
  "technique": "Progress-aware margin distillation for SLM agents",
  "family": "destilacao",
  "recommendation": "testar",
  "published": "2026-10-14",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.19439",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "ProD replaces plain supervised fine-tuning on teacher trajectories with an iterative distillation procedure that detects stagnation in student trajectories and adds an adaptive margin penalty to discourage unproductive loops. Costs include access to a teacher model for trajectory comparison, stagnation detection and margin computation during training, and iterative training rounds; on a single 24 GB GPU this is feasible for 0.6B-8B students with parameter-efficient fine-tuning but adds training complexity and time. It can fail if teacher trajectories are unavailable for the target domain, if the stagnation detector misfires and penalizes legitimate exploration, or if the student is too small to absorb the corrected behavior.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.19439",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Abstract claims ProD substantially mitigates progress stagnation and improves success rates and task-completion efficiency across 8 SLMs (0.6B-8B) on in-domain and out-of-domain benchmarks, but reports no specific numbers in the abstract.",
      "basis": "inferred",
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      "source_url": "https://arxiv.org/abs/2610.19439",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
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      "next_check": "",
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    },
    {
      "dimension": "compute",
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      "source_page": null,
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    },
    {
      "dimension": "latency",
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      "source_page": null,
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "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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}