---
id: 20261017-T0-08
title: "TurboMamba：用词汇记忆实现更快推理、更快训练和更好预测"
title_en: "TurboMamba: Faster Inference and Training with Lexical Memory"
url: https://ai.daily.yangsir.net/daily/20261017-T0-08
issue_date: 2026-10-17
publish_date: 2026-10-16T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2610.20682
---

# TurboMamba：用词汇记忆实现更快推理、更快训练和更好预测

TurboMamba是一种新的语言模型架构，目标是同时实现更快的推理、更快的训练和更好的token预测。其设计思路是：一个全局Mamba-3模型在每个完成的token块上运行一次，一个小型Transformer则利用这个全局上下文和块内更早的token进行预测。这种混合架构试图结合Mamba的高效序列建模与Transformer的局部注意力优势。目前该研究以预印本形式发布在arXiv上，具体性能数据需查阅论文全文。

## English Version

**TurboMamba: Faster Inference and Training with Lexical Memory**

TurboMamba is a new language model architecture aiming for faster inference, faster training, and better token prediction simultaneously. Its design runs a global Mamba-3 model once per completed token block, while a small Transformer uses this global context plus earlier tokens within the block for prediction. This hybrid approach seeks to combine Mamba's efficient sequence modeling with Transformer's local attention strengths. The work is available as an arXiv preprint; specific performance numbers require reading the full paper.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2610.20682)

**详情页**：https://ai.daily.yangsir.net/daily/20261017-T0-08

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