For deploying foundation models, practitioners increasingly need prescriptive scaling laws: given a pre training compute budget, what downstream accuracy is attainable with contemporary post training practice, and how stable is that mapping as the field evolves? Using large scale observational evaluations with 5k observational and 2k newly sampled data on model performance, we estimate capability boundaries, high conditional quantiles of benchmark scores as a function of log pre training FLOPs, via smoothed quantile regression with a monotone, saturating sigmoid parameterization. We validate the temporal reliability by fitting on earlier model generations and evaluating on later releases. Across various tasks, the estimated boundaries are mostly stable, with the exception of math reasoning that exhibits a consistently advancing boundary over time. We then extend our approach to analyze task dependent saturation and to probe contamination related shifts on math reasoning tasks. Finally, we introduce an efficient algorithm that recovers near full data frontiers using roughly 20% of evaluation budget. Together, our work releases the Proteus 2k, the latest model performance evaluation dataset, and introduces a practical methodology for translating compute budgets into reliable performance expectations and for monitoring when capability boundaries shift across time.

翻译：在基础模型部署过程中，从业者日益需要描述性缩放定律：给定预训练计算预算，采用当前后训练实践可获得的下游准确率是多少？该映射关系如何随领域发展保持稳定？通过对模型性能进行大规模观测评估（包含5千个观测样本和2千个新采样数据），我们通过采用单调饱和S型参数化的平滑分位数回归方法，估计了能力边界——即基准分数的高条件分位数与预训练浮点运算次数对数的函数关系。我们通过在早期模型代际上拟合、在后续发布模型上评估的方式验证了时间可靠性。跨多种任务中，估计边界基本保持稳定，但数学推理任务展现出随时间持续演进的能力边界。随后我们扩展该方法以分析任务依赖性饱和现象，并探究数学推理任务中与训练数据污染相关的边界偏移。最后，我们提出一种高效算法，仅需约20%的评估预算即可恢复近乎完整的数据边界。本研究同步发布了最新模型性能评估数据集Proteus 2k，并提出了一套实用方法论：既可将计算预算转化为可靠的性能预期，又能监测能力边界随时间推移发生的变化。