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English(EN) AdaThinking-E: One-Token Entropy Regulation for Adaptive Thinking

新框架使大型语言模型能够自适应地决定何时进行深度推理

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种名为AdaThinking-E的新型强化学习框架，旨在提高多模态大型语言模型的效率。该框架使模型能够根据问题的复杂性自适应地决定何时进行深度推理，而不是统一应用。通过调节决策Token的熵，AdaThinking-E使模型能够在无需外部标签的情况下学会何时思考，从而提高复杂任务的准确性并降低简单任务的计算开销。 AI

影响 该框架通过减少不必要的计算负载，有望带来更高效、响应更快的LLM应用。

排序理由 该集群包含一篇详细介绍LLM新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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

新框架使大型语言模型能够自适应地决定何时进行深度推理

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该集群包含一篇详细介绍LLM新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Zining Wang, Tongkun Guan, Boming Chen, Zhentao Guo, Jianqiang Liu, Chao Jin, Chen Duan, Kai Zhou, Pengfei Yan, Wei Shen, Xiaokang Yang ·

    AdaThinking-E：用于自适应思考的单Token熵调节

    arXiv:2608.26141v1 Announce Type: new Abstract: Multimodal large language models have demonstrated strong document reasoning capabilities by incorporating explicit thinking processes. While this capability significantly improves performance on challenging tasks, current models ap…

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