# AlterNet: 具有学习停止机制的自适应深度专家混合模型，用于弹性推理

> 全文为英语

**Published:** 2026年11月9日
**Author:** Alexander Kastius
**Source:** arXiv — cs.AI
**Language:** en-US
**HTML:** https://aiqazaqstan.com/zh/articles/alternet-adaptive-depth-mixture-of-experts-with-learned-halting-for-elas

arXiv:2611.08741v1 通告类型: 新 摘要: 大多数LLM架构具有在训练时确定的固定计算需求。我们提出了一种新架构，将专家混合架构与浅层专家和一种新颖的动态深度系统相结合，以使...

arXiv:2611.08741v1 Announce Type: new Abstract: Most LLM architectures have fixed compute demands determined at training time. We propose a novel architecture that combines the Mixture-of-Experts architecture with shallow experts and a novel dynamic-depth system to make the extent of a forward pass dynamically adjustable. Our approach lets the model automatically determine the compute needed for a given task, paired with a system to scale it down after training without retraining when the execution environment demands faster or cheaper inference. To achieve this, we introduce an iterative computation mechanism, sequence-level depth-based routing steered by a halting mechanism within the experts, a depth encoding that lets the model track its progress in the chain, and a regularization mechanism that incentivizes the model to use allocated compute efficiently. We present initial benchmarks and discuss future research directions.

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*内容来自原始来源： [arXiv — cs.AI](https://arxiv.org/abs/2611.08741). 查看原文.*

**标签：** #arXiv #Mixture-of-Experts #Elastic Inference #AlterNet

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