{
  "id": 5001692,
  "title": "Scaling Near-Optimal SFT-RL Annotation Budget Allocation from Small to Large LLMs",
  "url": "https://urgent.news/2026/09/01/scaling-near-optimal-sft-rl-annotation-budget-allocation-from-small",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-01T17:39:55.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.01573v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "How to divide a fixed annotation budget between supervised fine-tuning (SFT) and reinforcement learning (RL) during LLM post-training remains an open problem. Existing work characterizes only broad trends (e.g., SFT dominates in low-data regimes), lacks a principled allocation framework, and does not examine whether the optimal ratio transfers across model sizes. We frame this problem in terms of…",
  "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."
}