{
  "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": "2609.20082",
  "title": "MATCH: Model-Aware Tool Learning with Curriculum Scheduling and Hierarchically Gated Rewards",
  "summary": "It replaces fixed-threshold curricula and additive tool-call rewards in RL-based tool learning with a difficulty schedule that co-evolves with the policy and a gated reward that grants argument-level credit only when the tool name and argument keys are correct. The cost is a full GRPO training pipeline plus per-epoch reward-derived difficulty rescoring, which adds implementation complexity and training compute beyond simple supervised fine-tuning of tool-call data. It can fail if reward signals are sparse or noisy early in training, if the difficulty estimate oscillates and destabilizes the curriculum, or if the benchmark-specific reward design does not transfer to the reader's own tool schemas.",
  "rationale": "The method improves tool-calling accuracy but requires running RL training with custom reward and curriculum machinery, which strains a single 24 GB GPU and a small team, so it is worth tracking rather than adopting until simpler baselines are exhausted.",
  "technique": "Model-aware curriculum RL with hierarchically gated tool-call rewards",
  "family": "uso_de_ferramenta",
  "recommendation": "observar",
  "published": "2026-09-17",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.20082",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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    {
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      "statement": "MATCH reports 72.19% overall accuracy on API-Bank and 62.87% on BFCL V3, outperforming the supervised and RL-based baselines it compares against, with consistent gains across four backbones.",
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      "dimension": "operations",
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    {
      "dimension": "compatibility",
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    {
      "dimension": "security",
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    },
    {
      "dimension": "data_and_training",
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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",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
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}