{
  "id": 17985,
  "url": "https://arxiv.org/abs/2608.03571",
  "title": "Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning",
  "summary": "Recent works train agents by constructing large-scale multimodal environment pools. However, we find that simply increasing the number of multimodal environments does not always benefit. We further analyze the limitations in current multimodal environment distributions through a series of experiments. Based on these findings, we study how to build more effective training environment distributions from two dimensions: **diversity** and **difficulty structure**. For diversity, we propose **Ability",
  "authors": "Kejian Zhu, Zhuoran Jin, Dongqi Huang, Hongbang Yuan, Yupu Hao, Kang Liu",
  "category": "research",
  "topics": "agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-05T20:00:00.000Z",
  "fetched_at": "2026-08-11T05:10:37.351Z",
  "source_slug": "hf-daily",
  "source_name": "HuggingFace Daily Papers",
  "source_homepage": "https://huggingface.co/papers",
  "ethics_ai_record_url": "https://ethics.ai/record/17985",
  "original_url": "https://arxiv.org/abs/2608.03571",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}