ICML 2025收录，揭示数据质量如何决定大模型损失与缩放定律的深层关系。
arXiv:2502.12120v3 Announce Type: replace Abstract: Scaling laws guide the development of large language models (LLMs) by offering estimates for the o…
首次揭示LLM代理系统中技能缩放定律，基于15个模型、超百万数据点的实证研究
arXiv:2605.16508v1 Announce Type: new Abstract: As agent systems scale, skills accumulate into large reusable libraries, yet their scaling laws remain…
首次将神经缩放定律延伸至大语言模型碳排放预测，为绿色AI提供量化工具
arXiv:2508.06524v2 Announce Type: replace-cross Abstract: Large language models (LLMs) increasingly follow neural scaling laws that tie performance ga…
探讨心电图模型缩放定律：增大模型规模并非总能带来性能提升，挑战自然语言处理经验。
arXiv:2605.17276v1 Announce Type: new Abstract: While scaling laws have established a fundamental framework for foundation models in natural language …
从损失到利润：揭示大规模训练LLM的成本收益最优解，为AI投入产出提供理论框架。
arXiv:2605.16430v1 Announce Type: new Abstract: Scaling LLMs requires tremendous computational resources, and recent advances in AI have gone hand in …