{
  "id": 3875501,
  "title": "SWE-Prime: Fewer Trajectories, Better Performance",
  "url": "https://urgent.news/2026/08/27/swe-prime-fewer-trajectories-better-performance",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-27T17:58:10.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2608.27449v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "To improve large language models' ability to resolve real-world software issues, prior work has focused on constructing large-scale agent trajectory datasets and performing supervised fine-tuning (SFT) on successful trajectories. However, task success does not guarantee high-quality supervision: successful trajectories may still contain ineffective, redundant, or risky steps. Directly using such…",
  "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."
}