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English(EN) One Model, Multiple Compute Budgets: Multi-Ratio ConceptMoE for Flexible Compute Across Turns

新的ConceptMoE模型为LLM提供灵活的计算预算

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为Multi-Ratio ConceptMoE的新型模型架构，该架构允许单个模型在对话的多轮中以不同的计算预算运行。该方法在共享骨干网中使用2、4和8的token到concept压缩比，从而实现灵活的计算分配。该模型在PTBench和SFTBench等基准测试中展示了改进的准确性-计算权衡，并在CodeChat和CoSQL等任务上显著减少了与在独立模型之间路由相比的FLOPs。 AI

影响 通过动态调整每轮的计算预算，实现更高效的LLM推理，从而可能降低成本并提高对话式AI的性能。

排序理由 该集群包含一篇详细介绍新模型架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的ConceptMoE模型为LLM提供灵活的计算预算

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该集群包含一篇详细介绍新模型架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yiyang Fu, Zihao Huang, Dongchen Han, Shukai Gong, Zhen Dong, Qiyang Min, Daquan Zhou ·

    一个模型，多种计算预算：用于多轮灵活计算的多比率ConceptMoE

    arXiv:2610.14074v1 Announce Type: cross Abstract: Large language models typically support different inference budgets through separate model sizes or fixed compute configurations. In multi-turn interactions, however, compute demand can vary substantially across turns. Using a hig…

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