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English(EN) Switch-Reasoner: Learn When to Think in Multitask Mixtures via Reinforcement Learning

Switch-Reasoner框架学习多模态大语言模型何时需要推理

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了Switch-Reasoner，一个旨在提高多模态大语言模型（MLLMs）效率的新框架。该系统使用强化学习，使MLLMs能够根据任务的复杂性，自适应地选择直接回答还是显式推理。通过引入双层调节机制，Switch-Reasoner平衡了这些模式的使用，从而减少了不必要的计算，同时在各种多模态任务中保持高性能。 AI

影响 该框架通过减少计算开销，有望实现更高效、更具成本效益的多模态人工智能系统的部署。

排序理由 该集群包含一篇详细介绍多模态大语言模型新框架的研究论文。

在 arXiv cs.CV 阅读 →

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Switch-Reasoner框架学习多模态大语言模型何时需要推理

报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Yiyang Fang, Pei Fu, Jinjie Li, Jian Liang, Wenke Huang, Ruijie Luo, Shaojie Zhang, Jian Luan, Yi R. Fung, Mang Ye ·

    Switch-Reasoner: Learn When to Think in Multitask Mixtures via Reinforcement Learning

    arXiv:2607.08572v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) often follow a fixed Think-then-Answer paradigm, which is inefficient in heterogeneous multitask settings because simple inputs may not require explicit reasoning while difficult ones can ben…

  2. arXiv cs.CV TIER_1 English(EN) · Mang Ye ·

    Switch-Reasoner: Learn When to Think in Multitask Mixtures via Reinforcement Learning

    Multimodal Large Language Models (MLLMs) often follow a fixed Think-then-Answer paradigm, which is inefficient in heterogeneous multitask settings because simple inputs may not require explicit reasoning while difficult ones can benefit substantially from it. Learning when to thi…

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