{
  "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": "2610.11345",
  "title": "SynCo: Data Synthesis Co-Training for Self-Evolving LLMs via Multi-Agent Reinforcement Learning",
  "summary": "It replaces static or separately updated synthetic-data pipelines with a reinforcement-learning loop in which a task Synthesizer and a Reasoner are co-trained from shared rollout feedback. It costs two independently parameterized models, repeated rollouts per task, and a full multi-agent RL training stack, which exceeds a single 24 GB GPU and a limited cloud budget for any nontrivial model size. It can fail through reward mis-specification (teachability and task-quality signals rewarding easy or degenerate tasks), co-training instability between the two agents, and gains that may not transfer beyond mathematical reasoning where correctness is automatically verifiable.",
  "rationale": "The method requires sustained multi-agent RL training of two models, which is infrastructure the reader does not have, and its benefits are demonstrated only in a narrow verifiable-reward domain, so it should be monitored rather than adopted or tested now.",
  "technique": "Co-trained synthesizer-reasoner multi-agent RL",
  "family": "treino_eficiente",
  "recommendation": "observar",
  "published": "2026-10-08",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.11345",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 132,
  "citations": 0,
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      "dimension": "compatibility",
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      "dimension": "security",
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  "risks": [],
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
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  "equations_status": "not_fetched",
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