{
  "id": 10615509,
  "title": "KV-streams for Efficient Compaction in Agentic Reinforcement Learning",
  "url": "https://urgent.news/2026/09/28/kv-streams-for-efficient-compaction-in-agentic-reinforcement-learning",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-28T17:57:42.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.35750v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Scaling the horizon of agentic LLMs is bottlenecked by the need to fit ever longer context traces in GPU memory. Context compaction has been the most popular mechanism to alleviate this issue, keeping GPU memory constant for a given trace. Unfortunately, most compaction strategies rely on prefilling the LLM context many times over, hindering training throughput. To alleviate this bottleneck and…",
  "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."
}