论文

介绍 Gemini for Google (GfG)，一款面向 Google 内部软件工程生态的 Gemini 专用适配版本，涵盖从构建万亿 token 的专有数据集到采用可缓解灾难性遗忘的中段训练策略的完整过程。G Gemini for Google (GfG)}, an adaptation of Gemini specialized for Google's internal software engineering ecosystem, is introduced, from curating a trillion-token proprietary dataset to implementing a mid-training strategy that mitigates catastrophic forgetting.

对八款主流开源 MLOps 框架的实践使用与功能增强需求进行调查，结果显示 MLOps 框架很少被直接开箱即用，也较少集成进 GitHub Workflows，开发者更多通过其 API 在项目中实现自定义功能。Investigating the practical use and desired feature enhancements of eight popular open-source MLOps frameworks indicates that users mainly ask for enhancements to core features of the frameworks, but also better API exposure and CI/CD integration.

实验表明，DataEvolver 显著提升了数据质量，相比在原始数据上训练，下游 LLM 性能平均提升 10%，凸显了 LLM 与数据迭代协同演化的新机遇。Experiments show that DataEvolver substantially improves data quality and achieves an average 10\% gain in downstream LLM performance compared with training on original data, highlighting new opportunities for the iterative co-evolution of LLMs and data.

这些结果表明，Kueue、DAS 与 GAIE 等互补组件构成了一个高性能的协同平台，证明了 Kubernetes 能够作为承载高要求 GenAI 工作负载的统一底座。These findings illustrate that these complementary components (Kueue, DAS, and GAIE) form a cohesive, high-performance platform, proving Kubernetes' capability to serve as a unified foundation for demanding GenAI workloads.

本研究证实了激活函数之间存在统计显著的性能差异，从而再次确认了非饱和函数在深度架构中的必要性，并恢复了对此前文献的恰当历史归属。This study confirms a statistically significant performance variance among activations, thus reaffirming the necessity of non-saturating functions in deep architectures, and restores proper historical attribution to prior literature.

一种基于二部交互图上可微分消息传递的图自编码器框架，在标准协同过滤基准上表现出竞争力，并优于近期的 SOTA 方法。A graph auto-encoder framework based on differentiable message passing on the bipartite interaction graph that shows competitive performance on standard collaborative filtering benchmarks and outperforms recent state-of-the-art methods.

本文介绍 MLlib，Spark 的开源分布式机器学习库，可为多种学习场景提供高效功能，并包含若干底层的统计、优化和线性代数原语。MLlib is presented, Spark's open-source distributed machine learning library that provides efficient functionality for a wide range of learning settings and includes several underlying statistical, optimization, and linear algebra primitives.

本文详细阐述了驱动 PyTorch 实现的原则及其在架构中的体现，并解释了 runtime 关键组件的精心且务实的实现如何使其协同工作以获得出色的性能。This paper details the principles that drove the implementation of PyTorch and how they are reflected in its architecture, and explains how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance.

本文提出了一种基于迭代模型平均的深度网络联邦学习实践方法，并进行了广泛的实证评估，考虑了五种不同的模型架构和四个数据集。This work presents a practical method for the federated learning of deep networks based on iterative model averaging, and conducts an extensive empirical evaluation, considering five different model architectures and four datasets.

本文提出了一种通过创建在所有边缘设备之间全局共享的小型数据子集来改进非独立同分布数据训练的策略，并表明在 CIFAR-10 数据集上，仅共享 5% 的全局数据即可将准确率提升 30%。This work presents a strategy to improve training on non-IID data by creating a small subset of data which is globally shared between all the edge devices, and shows that accuracy can be increased by 30% for the CIFAR-10 dataset with only 5% globally shared data.

本文提出一种权重裁剪的替代方案：对 critic 相对于其输入的梯度范数施加惩罚。其性能优于标准 WGAN，能以几乎无需调参的方式稳定训练多种 GAN 架构。This work proposes an alternative to clipping weights: penalize the norm of gradient of the critic with respect to its input, which performs better than standard WGAN and enables stable training of a wide variety of GAN architectures with almost no hyperparameter tuning.