{
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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."
  },
  "builder_review": null,
  "arxiv_id": "2610.30319",
  "title": "TerraTransfer++: Demonstration-Free End-to-End Planner",
  "summary": "The method replaces imitation learning on curated expert driving logs with a self-play policy pretrained in a vectorized simulator, distilled via flow matching and adapter-based students on a frozen vision backbone, requiring only paired image/scene-state frames rather than demonstrations. The cost is substantial: it presupposes access to a high-throughput vectorized driving simulator, a pretrained vision backbone, photorealistic closed-loop evaluation infrastructure (3D Gaussian splatting), and multi-stage distillation with custom losses, all outside a 24 GB GPU budget and a small team's typical autonomous-driving scope. It can fail through sim-to-real gap: a policy whose entire supervision traces to simulator self-play may encode simulator-specific dynamics, and benchmark gains on HUGSim/NAVSIM do not establish on-road safety.",
  "rationale": "The reader is an AI/ML engineer with one 24 GB GPU and no cluster, while this work targets autonomous-driving teams with simulator infrastructure and benchmark pipelines, making it relevant to watch but not adoptable at the reader's scale.",
  "technique": "Self-play distillation into end-to-end driving planners",
  "family": "destilacao",
  "recommendation": "observar",
  "published": "2026-10-21",
  "as_of": "2026-10-24",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.30319",
  "independent_implementations": 5,
  "total_implementations": 6,
  "stars": 171,
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