Scaling laws for large language models (LLMs) predict model performance based on parameters like size and training data. However, differences in training configurations and data processing across model families lead to significant variations in benchmark performance, making it difficult for a single scaling law to generalize across all LLMs. On the other hand, training family-specific scaling laws requires training models of varying sizes for every family. In this work, we propose Skills Scaling Laws (SSLaws, pronounced as Sloth), a novel scaling law that leverages publicly available benchmark data and assumes LLM performance is driven by low-dimensional latent skills, such as reasoning and instruction following. These latent skills are influenced by computational resources like model size and training tokens, but with varying efficiencies across model families. Sloth exploits correlations across benchmarks to provide more accurate and interpretable predictions while alleviating the need to train multiple LLMs per family. We present both theoretical results on parameter identification and empirical evaluations on 12 prominent benchmarks, from Open LLM Leaderboard v1/v2, demonstrating that Sloth predicts LLM performance accurately and offers insights into scaling behaviors for complex downstream tasks, increased test-time compute, and compute-optimal scaling of skills.

翻译：大型语言模型（LLM）的扩展律通过模型规模和训练数据等参数预测模型性能。然而，不同模型家族在训练配置和数据处理上的差异导致基准测试性能存在显著波动，使得单一扩展律难以泛化至所有LLM。另一方面，为每个模型家族训练特定扩展律需要训练不同规模的模型。本研究提出技能扩展律（SSLaws，简称Sloth），这是一种利用公开基准数据的新型扩展律，其假设LLM性能由低维潜在技能（如推理和指令遵循）驱动。这些潜在技能受模型规模和训练词元等计算资源影响，但不同模型家族的影响效率存在差异。Sloth通过挖掘跨基准测试的相关性，在减少每个模型家族需训练多个LLM的同时，提供更精准且可解释的预测。我们展示了参数识别的理论结果，并在Open LLM Leaderboard v1/v2的12个重要基准上进行了实证评估，证明Sloth能准确预测LLM性能，并为复杂下游任务的扩展行为、增加的测试时计算量以及技能的计算最优扩展提供深入见解。