{
  "id": 16583,
  "url": "https://arxiv.org/abs/2608.01804v2",
  "title": "LEAP: Lean Environment-Feedback via Adaptive Pruning for Code RL in GPU Kernel Generation",
  "summary": "Post-training large language models (LLMs) via reinforcement learning (RL) has significantly advanced code generation capabilities. To bypass the heavy memory footprint of critic networks, current state-of-the-art frameworks leverage critic-free paradigms like Group Relative Policy Optimization (GRPO) tied to rule-based verification sandboxes. However, applying these frameworks to low-level systems programming, such as CUDA kernel generation-presents severe challenges: binary pass/fail rewards i",
  "authors": "Tankun Li, Zhi Chen, Yaohua Tang",
  "category": "research",
  "topics": "regulation,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-03T07:12:52.000Z",
  "fetched_at": "2026-08-05T05:10:44.550Z",
  "source_slug": "arxiv-cslg",
  "source_name": "arXiv cs.LG",
  "source_homepage": "https://arxiv.org/list/cs.LG/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/16583",
  "original_url": "https://arxiv.org/abs/2608.01804v2",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}