{
  "id": 8822652,
  "title": "CodeMidas: Scaling Agentic Coding RL Environments from Code Itself",
  "url": "https://urgent.news/2026/09/18/codemidas-scaling-agentic-coding-rl-environments-from-code-itself",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-18T17:55:17.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.22068v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Training capable coding agents via reinforcement learning (RL) requires diverse tasks with reliable verifiers. Open-source codebases offer a rich source of such tasks, while existing methods typically rely on development artifacts such as issues and commits, limiting the range of tasks that can be extracted. To better scale RL environments, we present CodeMidas, an agentic pipeline that turns…",
  "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."
}