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新的ADaPT框架提高了大型推理模型的效率

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出了一种名为ADaPT的新型框架，旨在提高大型推理模型的效率。该方法通过使用模式选择令牌在训练期间分离效率和正确性信号，从而实现快速或慢速推理路径。ADaPT能够在推理时对性能-效率权衡进行细粒度控制，在保持各种基准测试中强大推理能力的同时，显著降低了计算成本。 AI

影响 这一新框架可能导致大型推理模型在实际应用中更具成本效益的部署和使用。

排序理由 该集群包含一篇研究论文，详细介绍了一种提高AI模型效率的新方法。

在 arXiv cs.LG 阅读 →

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新的ADaPT框架提高了大型推理模型的效率

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该集群包含一篇研究论文，详细介绍了一种提高AI模型效率的新方法。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Tingyun Li, Zishang Jiang, Jinyi Han, Xinyi Wang, Sihang Jiang, Han Xia, Zhaoqian Dai, Shuguang Ma, Fei Yu, Jiaqing Liang, Yanghua Xiao ·

    ADaPT：高效大型推理模型的令牌级解耦

    arXiv:2606.19919v1 Announce Type: new Abstract: Large reasoning models rely on long chain-of-thought to achieve strong performance, but applying such reasoning uniformly incurs high computational cost. Existing efficiency-oriented methods attempt to shorten or mix reasoning strat…

  2. arXiv cs.LG TIER_1 English(EN) · Yanghua Xiao ·

    ADaPT：面向高效大型推理模型的 token 级解耦

    Large reasoning models rely on long chain-of-thought to achieve strong performance, but applying such reasoning uniformly incurs high computational cost. Existing efficiency-oriented methods attempt to shorten or mix reasoning strategies, yet often degrade reasoning capability. W…

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