{
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    "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."
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  "builder_review": null,
  "arxiv_id": "2610.18613",
  "title": "Does self-distillation improve reasoning?",
  "summary": "Self-distillation (SDPO, OPSD) trains a model to imitate its own predictions conditioned on privileged information, positioned as a more sample-efficient replacement for reinforcement learning from rewards; this paper is a critical re-evaluation, not a new method. The method costs a training run with privileged conditioning and adds distillation pipeline complexity over standard supervised fine-tuning, while this paper finds its gradients align weakly with reward-optimizing policy gradients. What can fail: reported gains do not transfer to unseen problems, are explained by overfitting or transferring existing behavior rather than using privileged information, and so the approach may deliver no out-of-distribution improvement in production.",
  "rationale": "This is a negative-results replication showing self-distillation methods do not reliably generalize or optimize reward, so it primarily warns the reader against adopting these techniques rather than offering something actionable for constrained production use.",
  "technique": "Self-distillation for reasoning (SDPO/OPSD) reassessed",
  "family": "destilacao",
  "recommendation": "observar",
  "published": "2026-10-13",
  "as_of": "2026-10-18",
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  "source_url": "https://arxiv.org/abs/2610.18613",
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