{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "An assistant chooses the wrong API or sends arguments your product cannot use.",
    "change": "Apply the proposed tool-selection or argument-generation change in a sandbox.",
    "baseline": "Your current agent with the same tool schemas and task 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 completed actions, invalid calls, retries, latency, and cost per completed task.",
    "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": "2609.17848",
  "title": "SFT or RL for Tool-Calling Agents? A Controlled Study Across Data, Method, and Scale",
  "summary": "This controlled comparison replaces the default assumption that RL post-training (GRPO) or SFT-then-RL pipelines are needed for tool-calling agents, showing simple LoRA SFT is the stronger default and also beats full-parameter fine-tuning. The cost is modest: LoRA SFT runs on a single 24 GB GPU for small-to-mid models, while GRPO adds training complexity, compute, and pipeline overhead for gains that are largely confined to cross-dataset transfer and average under one point. Failure modes include overfitting to the training dataset's tool format when transfer matters, which dataset mixing mitigates, and possible degradation of pretrained agentic behavior if full fine-tuning is used instead of LoRA.",
  "rationale": "For a constrained-infrastructure team, LoRA SFT is the cheapest method that this controlled study shows wins in-distribution across the full model range, with GRPO justified only as a small-scale experiment when out-of-distribution tool transfer is a production requirement.",
  "technique": "LoRA SFT versus GRPO for tool-calling agents",
  "family": "uso_de_ferramenta",
  "recommendation": "adotar",
  "published": "2026-09-15",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.17848",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
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  "report_available": false,
  "validation_tier": "unknown",
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