{
  "id": 6006,
  "url": "https://arxiv.org/abs/2604.10547v2",
  "title": "Agent^2 RL-Bench: Can LLM Agents Engineer Agentic RL Post-Training?",
  "summary": "We introduce Agent2 RL-Bench, a compact diagnostic benchmark for evaluating agentic RL post-training, which tests whether LLM agents can autonomously design, implement, debug, and execute post-training pipelines that improve foundation models. RL post-training increasingly drives model alignment and specialization, yet existing benchmarks are largely static, rewarding supervised fine-tuning or script generation without assessing an agent's ability to close an interactive RL loop. Agent2 RL-Bench",
  "authors": "Wanyi Chen, Xiao Yang, Xu Yang, Tianming Sha, Qizheng Li, Zhuo Wang et al.",
  "category": "research",
  "topics": "safety-alignment,healthcare,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-12T09:35:27.000Z",
  "fetched_at": "2026-07-14T16:32:11.183Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6006",
  "original_url": "https://arxiv.org/abs/2604.10547v2",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}