---
id: 20260609-T0-11
title: "PoLar框架：让LLM动态选择推理路径"
title_en: "PoLar Framework Enables Dynamic LLM Layer Selection"
url: https://ai.daily.yangsir.net/daily/20260609-T0-11
issue_date: 2026-06-09
publish_date: 2026-06-08T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2606.06574
---

# PoLar框架：让LLM动态选择推理路径

arXiv研究揭示LLM存在训练后动态层选择能力。传统模型按固定顺序执行所有层，但新发现显示预训练层可灵活组合形成动态程序。这种非循环执行模式在保持性能的同时降低计算开销。研究团队通过实验验证了'层程序化'现象，为构建更高效的推理架构提供新思路。该方法或将改变大模型部署方式，实现资源按需分配。

## English Version

**PoLar Framework Enables Dynamic LLM Layer Selection**

ArXiv study reveals LLMs have post-training dynamic layer selection capability. Traditional models execute all layers in fixed order, but research shows pretrained layers can flexibly combine into dynamic programs. This non-recurrent execution reduces computation while maintaining performance. The team experimentally verified 'layer programming' phenomenon, offering new insights for efficient inference architectures. This could change LLM deployment, enabling on-demand resource allocation.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2606.06574)

**详情页**：https://ai.daily.yangsir.net/daily/20260609-T0-11

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