{
  "id": 6382925,
  "title": "The Surprising Effectiveness of Approximate Value Iteration in Self-Play",
  "url": "https://urgent.news/2026/09/08/the-surprising-effectiveness-of-approximate-value-iteration-in-self",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-08T17:37:19.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.09094v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Combining search with function approximation has driven major advances in game-playing programs, making self-play algorithms more competitive than ever. Still, the computational overhead of the most popular methods, based on Monte Carlo Tree Search (MCTS), can be substantial. In this work, we investigate whether simpler methods remain competitive in non-trivial, moderately sized games such as…",
  "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."
}