{ "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": "2608.24571", "title": "Joint Optimization of Tool Creation and Use for Large Language Model Agents", "summary": "SMITH replaces inference-time prompting of a frozen LLM to write and use tools with a single policy jointly trained via RL on build and use tasks, with separate reward axes for schema, code, and outcome failures. Adoption costs an RL training pipeline with exact verifiers for each task, curated build/use rollouts, and multi-axis reward computation—beyond a single 24 GB GPU for most teams without significant engineering effort. It can fail on tasks lacking automatic verifiers, when generated tools contain subtle bugs that propagate to users, and when tool pools overfit to the 13 training tasks despite reported out-of-domain transfer.", "rationale": "The gains are real and quality-oriented, but the method requires RL training infrastructure and verified task suites that exceed constrained production setups, so tracking it until training-as-a-service or released checkpoints mature is the rational posture.", "technique": "Joint RL training of tool creation and tool use", "family": "uso_de_ferramenta", "recommendation": "observar", "published": "2026-08-25", "as_of": "2026-10-18", "editorial_status": "indexed", "source_url": "https://arxiv.org/abs/2608.24571", "independent_implementations": 0, "total_implementations": 1, "stars": 28, "citations": 0, "claims": [ { "claim_id": "claim-01", "statement": "SMITH replaces inference-time prompting of a frozen LLM to write and use tools with a single policy jointly trained via RL on build and use tasks, with separate reward axes for schema, code, and outcome failures. 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