专攻低带宽场景的流媒体压缩引擎，视频富媒体最高减96%体积
Article URL: https://github.com/axmstandart1-beep/axioma/tree/main Comments URL: https://news.ycombinator.com/item?id=49124066 Points: 2 # Comments: 1
突破带宽限制，DiLoCo类方法如何平衡通信与性能，提升LLM预训练效率
arXiv:2508.15706v3 Announce Type: replace Abstract: Communication-efficient distributed training algorithms (e.g., DiLoCo) have received considerable …
提出一种压缩模型大小与类别数双重带宽瓶颈的联邦蒸馏方法，大幅提升通信效率。
arXiv:2607.00173v1 Announce Type: new Abstract: Federated learning is bandwidth-bound on two orthogonal axes: model size, which limits how often param…
这篇论文提出统一本地通信与更新策略，旨在提升大模型预训练的通信效率，分布式训练的新视角。
arXiv:2606.11081v1 Announce Type: cross Abstract: Communication-efficient pre-training of LLMs is increasingly important as training draws on compute …