{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Your coding assistant makes changes that still need substantial repair before merging.",
    "change": "Try the technique on held-out issues from a repository with reliable tests.",
    "baseline": "The same coding agent, repository snapshot, tools, and token budget.",
    "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": "Correct patches, regression failures, human repair time, and total cost per accepted change.",
    "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": "2610.28069",
  "title": "Training the Whole Agent, Not Just the Model",
  "summary": "The method replaces a fixed, human-designed agent harness with tools, hooks, and task-specific skills evolved by frozen teacher models alongside GRPO policy training, and its deployment artifact is the trained model plus the evolved harness. The full pipeline costs RL training of a 9B policy, teacher-model inference for skill and tool generation, and an accept-reject loop gated on observed pass rate and trainable rate, which exceeds a single 24 GB GPU; the harness-evolution component alone is API-feasible. Failure modes include harness overfitting to training tasks (the paper itself reports up to 7.7 points lost if the evolved harness is removed at deployment), acceptance decisions driven by noisy pass-rate estimates, and tool changes that help the current policy but degrade as the policy drifts.",
  "rationale": "The reader cannot run the full RL co-training pipeline on constrained infrastructure, but evolving tools and hooks via a teacher API gated on measured pass rate is testable at small scale, provided gains are validated on the reader's own tasks since the paper's headline numbers depend on RL training.",
  "technique": "Co-adapting agent harness during RL training",
  "family": "agentes_de_codigo",
  "recommendation": "testar",
  "published": "2026-10-20",
  "as_of": "2026-10-21",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.28069",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
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      "dimension": "operations",
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    {
      "dimension": "compatibility",
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      "dimension": "security",
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      "dimension": "reproducibility",
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  "risks": [],
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
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  "equations_status": "not_fetched",
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}