Discovering scaling laws for predicting model performance at scale is a fundamental and open-ended challenge, mostly reliant on slow, case specific human experimentation. To investigate the potential for LLMs to automate this process, we collect over 5,000 experiments from existing literature and curate eight diverse scaling law discovery tasks. While existing agents struggle to produce accurate law formulas, this paper introduces SLDAgent, an evolution-based agent that co-optimize the scaling law model and the parameters, enabling it to autonomously explore complex relationships between variables. For the first time, we demonstrates that SLDAgent can automatically discover laws that exhibit consistently more accurate extrapolation than their established, human-derived counterparts across all tasks. Through comprehensive analysis, we elucidate why these discovered laws are superior and verify their practical utility in both pretraining and finetuning applications. This work establishes a new paradigm for agentic scientific discovery, showing that AI systems can understand their own scaling behavior, and can contribute novel and practical knowledge back to the research community.

翻译：发现用于预测模型在大规模下性能的缩放定律是一个基础且开放性的挑战，目前主要依赖于缓慢、针对特定案例的人工实验。为了探究大型语言模型自动化此过程的潜力，我们从现有文献中收集了超过5000个实验，并策划了八个多样化的缩放定律发现任务。虽然现有智能体难以生成准确的法律公式，但本文引入了SLDAgent，这是一种基于进化的智能体，能够协同优化缩放定律模型及其参数，使其能够自主探索变量间的复杂关系。我们首次证明，在所有任务中，SLDAgent能够自动发现的定律，其外推预测的准确性始终优于已确立的、由人类推导的对应定律。通过全面分析，我们阐明了这些发现的定律为何更优，并验证了它们在预训练和微调应用中的实际效用。这项工作为智能体驱动的科学发现建立了一个新范式，表明人工智能系统能够理解自身的缩放行为，并能够向研究社区贡献新颖且实用的知识。