English(EN) AdaptR1: Reinforcement Learning Based Adaptive Interleaved Thinking in Multi-hop Question Answering

AdaptR1框架通过强化学习降低大型语言模型推理成本

作者 PulseAugur 编辑部 · [2 个来源] · 2026-05-29 09:34

研究人员开发了AdaptR1，一个新颖的框架，它使用强化学习来优化大型语言模型在多跳问答中的推理。与先前在查询级别做出单一决策的方法不同，该方法在每个步骤动态分配推理预算。AdaptR1显著减少了生成的“思考令牌”数量，从而降低了推理成本，同时在HotpotQA等任务上保持或提高了性能。 AI

影响通过优化令牌使用，降低了复杂大型语言模型推理任务的推理成本。

排序理由该集群包含一篇详细介绍大型语言模型新研究框架的学术论文。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv cs.CL TIER_1 English(EN) · Yuxin Wang, Jiahao Lu, Qifeng Wu, Shicheng Fang, Chuanyuan Tan, Yining Zheng, Xuanjing Huang, Xipeng Qiu · 2026-06-01 04:00

AdaptR1：基于强化学习的多跳问答自适应交错思考

arXiv:2605.31062v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved remarkable performance in complex reasoning tasks through Chain-of-Thought (CoT) prompting. However, this approach often leads to ``over-thinking,'' where models generate unnecessarily long…
arXiv cs.CL TIER_1 English(EN) · Xipeng Qiu · 2026-05-29 09:34

AdaptR1：基于强化学习的多跳问答自适应交错思考

Large Language Models (LLMs) have achieved remarkable performance in complex reasoning tasks through Chain-of-Thought (CoT) prompting. However, this approach often leads to ``over-thinking,'' where models generate unnecessarily long reasoning traces for simple queries and incur a…