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English(EN) Route-and-Generate: KL-Regularized Router Improvement for Reasoning Routing in LLMs

新的LLM训练框架Route-and-Generate优化推理效率

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种名为Route-and-Generate (Rouge) 的新型训练框架，旨在增强大型语言模型 (LLM) 的推理能力。该框架允许LLM动态确定不同输入的必要推理水平，通过在各种推理格式中分配固定的采样预算来优化性能。该系统使用通过KL-正则化策略改进更新的路由策略，鼓励对简单任务给出简洁的答案，对复杂任务进行更广泛的推理，从而提高基准测试的准确性-效率权衡。 AI

影响 这一新的训练框架通过优化推理过程，有望带来更高效、更准确的LLM。

排序理由 该集群包含一篇详细介绍LLM新训练方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 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LLM训练框架Route-and-Generate优化推理效率

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该集群包含一篇详细介绍LLM新训练方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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完整方法见我们的编辑标准。

报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Tien-Phat Nguyen, Truong Nguyen, Thanh-Thien Le, Hoang Phan, Thien Huu Nguyen, Linh Ngo Van, Trung Le ·

    Route-and-Generate: KL-Regularized Router Improvement for Reasoning Routing in LLMs

    arXiv:2610.30351v1 Announce Type: cross Abstract: Large language models often apply complex reasoning indiscriminately, leading to overthinking on simple tasks and underthinking on harder ones. We present Route-and-Generate (Rouge), a hierarchical training framework that enables …

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