论文

研究表明，机器学习在公共部门的成功，将更少依赖模型准确率的突破，而更多依赖机构能否构建出透明、可复现、可问责且受公民信任的数据基础设施。It is shown that the success of machine learning in the public sector will depend less on breakthroughs in model accuracy and more on the ability of institutions to engineer transparent, reproducible, and accountable data infrastructures that citizens can trust.

WARP：一个直接从已发布权重还原微调模型训练数据混合比例的框架，抽取几何特征并映射至各领域占比，可采用无参数 softmax 读出器，或基于合成混合训练的 MLP 投影器。WARP is introduced, a framework that recovers a fine-tuned model's training mixtures directly from its released weights and extracts geometric features and maps them to domain proportions using either a parameter-free softmax readout or an MLP projector trained on synthetic mixtures.

本文提出 Randomized YaRN，一种通过将基于 YaRN 的位置外推与随机位置编码和长度课程相结合来提升长度泛化能力的训练方法，表明渐进式地将模型暴露于分布外位置分布是实现可泛化长上下文推理的有效方案。Randomized YaRN is proposed, a training method that improves length generalization by combining YaRN-based positional extrapolation with randomized positional encoding and a length curriculum, and suggests that progressively exposing models to OOD positional distributions provides an effective recipe for generalizable long-context reasoning.