{
  "id": 18258983,
  "title": "On Turn-Level Best-of-N Sampling for Scaling LLM Agents",
  "url": "https://urgent.news/2026/11/04/on-turn-level-best-of-n-sampling-for-scaling-llm-agents",
  "topic": "ai",
  "section": "AI",
  "published": "2026-11-04T18:23:14.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2611.06868v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "In this work, we evaluate a simple inference-time scaling approach based on \\textit{turn-level best-of-N sampling} (\\method) for large language model (LLM) agents on complex, long-horizon tasks. We instantiate this using a \\textit{generator-selector} framework with two LLMs; at each turn, a \\emph{generator} model generates $N$ candidate actions and a \\emph{selector} model selects one of them,…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}