Understanding how language model performance varies with scale is critical to benchmark and algorithm development. Scaling laws are one approach to building this understanding, but the requirement of training models across many different scales has limited their use. We propose an alternative, observational approach that bypasses model training and instead builds scaling laws from ~80 publically available models. Building a single scaling law from multiple model families is challenging due to large variations in their training compute efficiencies and capabilities. However, we show that these variations are consistent with a simple, generalized scaling law where language model performance is a function of a low-dimensional capability space, and model families only vary in their efficiency in converting training compute to capabilities. Using this approach, we show the surprising predictability of complex scaling phenomena: we show that several emergent phenomena follow a smooth, sigmoidal behavior and are predictable from small models; we show that the agent performance of models such as GPT-4 can be precisely predicted from simpler non-agentic benchmarks; and we show how to predict the impact of post-training interventions like Chain-of-Thought and Self-Consistency as language model capabilities continue to improve.

翻译：理解语言模型性能如何随规模变化对于基准测试和算法开发至关重要。标度定律是构建这种理解的一种方法，但跨多个不同规模训练模型的需求限制了其应用。我们提出了一种替代性的观测方法，该方法绕过模型训练，转而从约80个公开可用的模型中构建标度定律。由于不同模型系列在训练计算效率和能力上存在巨大差异，从多个模型系列构建单一的标度定律具有挑战性。然而，我们证明这些差异与一个简单的广义标度定律一致，其中语言模型性能是低维能力空间的函数，不同模型系列仅在将训练计算转化为能力的效率上有所不同。利用这种方法，我们展示了复杂标度现象令人惊讶的可预测性：我们表明若干涌现现象呈现出平滑的S形行为，并且可以从小型模型中预测；我们展示像GPT-4这样的模型的代理性能可以从更简单的非代理基准测试中精确预测；我们还展示了如何预测链式思维和自一致性等训练后干预措施在语言模型能力持续提升时的影响。