{
  "video_id": "8685d8aee6c1c7e9",
  "channel": "export-arxiv-org-rss-cs-ai",
  "title": "RODS: Reward-Driven Online Data Synthesis for Multi-Turn Tool-Use Agents",
  "scores": {
    "depth": 3,
    "novelty": 3,
    "aria_relevance": 3,
    "production_ready": 2
  },
  "junk_penalty": 0,
  "avg_score": 2.75,
  "effective_score": 2.75,
  "verdict": "promote",
  "one_line_reason": "Cutting-edge multi-agent RL sampling theory (RODS) with direct relevance to Aria's agent training loops, online data synthesis, and sample efficiency—mathematically grounded (Popoviciu bounds), novel boundary detection via reward variance, 20x sample reduction. Highly relevant for RL-based agent cap",
  "model": "claude-haiku-4-5-20251001",
  "cost_usd": 0.0019190000000000001,
  "triaged_at": "2026-06-18T15:03:42.958541+00:00"
}