{
  "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": "2609.27532",
  "title": "ProCredit: From Outcome Rewards to Progress Credit in Agentic Reinforcement Learning",
  "summary": "ProCredit replaces terminal outcome-only rewards in agentic reinforcement learning with per-turn rewards computed by re-running the task's acceptance checks on intermediate environment states and crediting each turn by its change in verified progress. It costs repeated execution of the acceptance-check suite after every turn, increasing training compute and environment interaction, and requires modifying the RL pipeline to propagate turn-level credit; the ablation shows naive use of final progress as a trajectory score yields nothing. It fails where tasks lack programmatically verifiable intermediate acceptance checks, where checks are expensive or non-idempotent to run on partial states, or where progress measures are non-monotonic and can be gamed by the policy.",
  "rationale": "The method requires running RL training with a live checkable environment, which exceeds the reader's constrained infrastructure and team capacity today, though the underlying idea of reusing acceptance checks as intermediate verifiable signals is worth tracking for future fine-tuning work.",
  "technique": "Per-turn progress credit from re-run acceptance checks",
  "family": "treino_eficiente",
  "recommendation": "observar",
  "published": "2026-09-23",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.27532",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "ProCredit replaces terminal outcome-only rewards in agentic reinforcement learning with per-turn rewards computed by re-running the task's acceptance checks on intermediate environment states and crediting each turn by its change in verified progress. It costs repeated execution of the acceptance-check suite after every turn, increasing training compute and environment interaction, and requires modifying the RL pipeline to propagate turn-level credit; the ablation shows naive use of final progress as a trajectory score yields nothing. It fails where tasks lack programmatically verifiable intermediate acceptance checks, where checks are expensive or non-idempotent to run on partial states, or where progress measures are non-monotonic and can be gamed by the policy.",
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      "result": "",
      "baseline": "",
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      "source_url": "https://arxiv.org/abs/2609.27532",
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    },
    {
      "claim_id": "claim-02",
      "statement": "Exceeds the strongest outcome-reward baseline by 4.1 percentage points in task completion at 4B scale on AppWorld, with consistent gains at all three model scales and in a second environment.",
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      "source_url": "https://arxiv.org/abs/2609.27532",
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      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    }
  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
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  "equations_source_sha256": "",
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}