{
  "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": "2610.22976",
  "title": "Towards Hyperparameter Transfer for Differentially Private Optimization",
  "summary": "DP-μP replaces per-width learning-rate sweeps (and standard μP, which the paper shows breaks under DP) with layer-wise learning-rate scaling rules derived from a signal-to-noise condition for gradient clipping and noise injection. The cost is implementing a modified parametrization and spectral scaling scheme in the training code, which adds complexity and constrains architectural choices, though inference cost is unchanged. The method can fail when assumptions behind the SNR condition are violated, such as atypical clipping thresholds, noise multipliers, or architectures far from those validated, and the results concern width transfer from scratch rather than fine-tuning of pretrained models.",
  "rationale": "The reader does not train foundation models and typically fine-tunes pretrained ones, a setting this width-scaling transfer result does not address, so the technique is relevant only if DP training from scratch becomes part of the workload.",
  "technique": "DP-μP: hyperparameter transfer for DP training",
  "family": "treino_eficiente",
  "recommendation": "observar",
  "published": "2026-10-16",
  "as_of": "2026-10-19",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.22976",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 4,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "DP-μP replaces per-width learning-rate sweeps (and standard μP, which the paper shows breaks under DP) with layer-wise learning-rate scaling rules derived from a signal-to-noise condition for gradient clipping and noise injection. The cost is implementing a modified parametrization and spectral scaling scheme in the training code, which adds complexity and constrains architectural choices, though inference cost is unchanged. The method can fail when assumptions behind the SNR condition are violated, such as atypical clipping thresholds, noise multipliers, or architectures far from those validated, and the results concern width transfer from scratch rather than fine-tuning of pretrained models.",
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      "statement": "Zero-shot learning-rate transfer across model widths on MLP, ViT, and GPT-2 under DP-SGD, DP-Adam, and DP-Muon, matching or exceeding individually tuned baselines while significantly reducing tuning compute; no multiplicative factor is reported in the abstract.",
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      "source_url": "https://arxiv.org/abs/2610.22976",
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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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  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
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  "equations_status": "not_fetched",
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