{
  "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": "2609.22068",
  "title": "CodeMidas: Scaling Agentic Coding RL Environments from Code Itself",
  "summary": "The method replaces RL environment construction that depends on development artifacts (issues, commits) with an agentic pipeline that derives behavioral specifications, tests, and validated tasks directly from source code. It costs substantial agentic compute at every construction stage plus the infrastructure to run GRPO training on a large model, which exceeds a single 24 GB GPU and a limited budget. It can fail through unreliable auto-generated verifiers, tasks that pass spuriously under repeated rollouts, and quality degradation if the execution-based filtering is insufficient.",
  "rationale": "The value of this paper is a scalable recipe for producing RL training tasks, but adopting it requires environment-building fleets and RL training runs that constrained infrastructure cannot support, so the reader should track it rather than deploy it.",
  "technique": "Agentic pipeline generating RL coding environments from source code",
  "family": "agentes_de_codigo",
  "recommendation": "observar",
  "published": "2026-09-18",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.22068",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 2,
  "citations": null,
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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": [],
  "minimum_test": [],
  "open_questions": [],
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  "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": "",
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}