title: Think Only When You Need with Large Hybrid-Reasoning Models
summary: Recent Large Reasoning Models (LRMs) have shown substantially improved
  reasoning capabilities over traditional Large Language Models (LLMs) by
  incorporating extended thinking processes prior to producing final responses.
  However, excessively lengthy thinking introduces substantial overhead in terms
  of token consumption and latency, which is particularly unnecessary for simple
  queries. In this work, we introduce Large Hybrid-Reasoning Models (LHRMs), the
  first kind of model capable of adaptively determining whether to perform
  thinking based on the contextual information of user queries. To achieve this,
  we propose a two-stage training pipeline comprising Hybrid Fine-Tuning (HFT) as
  a cold start, followed by online reinforcement learning with the proposed
  Hybrid Group Policy Optimization (HGPO) to implicitly learn to select the
  appropriate thinking mode. Furthermore, we introduce a metric called Hybrid
  Accuracy to quantitatively assess the model's capability for hybrid thinking.
  Extensive experimental results show that LHRMs can adaptively perform hybrid
  thinking on queries of varying difficulty and type. It outperforms existing
  LRMs and LLMs in reasoning and general capabilities while significantly
  improving efficiency. Together, our work advocates for a reconsideration of the
  appropriate use of extended thinking processes and provides a solid starting
  point for building hybrid thinking systems.
paper_authors._id: 682d69ed056cf7b86cd8d6ec
paper_authors.name: Lingjie Jiang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-21T08:40:17.384Z
paper_authors._id: 66ab80e9bfb7d73a56bc293c
paper_authors.avatarUrl: /avatars/9644266304c832c74ef572b5eb2d9468.svg
paper_authors.fullname: Jack
paper_authors.type: user
paper_authors.user: lingjie23
paper_authors._id: 682d69ed056cf7b86cd8d6ed
paper_authors.name: Xun Wu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-21T08:40:20.741Z
paper_authors._id: 62d1227384bfbee86b6eec56
paper_authors.avatarUrl: /avatars/84435f9768a76c0fe9d404dfc2d70be3.svg
paper_authors.fullname: Xun Wu
paper_authors.type: user
paper_authors.user: YUSHUIWX
paper_authors._id: 682d69ed056cf7b86cd8d6ee
paper_authors.name: Shaohan Huang
paper_authors._id: 682d69ed056cf7b86cd8d6ef
paper_authors.name: Qingxiu Dong
paper_authors._id: 682d69ed056cf7b86cd8d6f0
paper_authors.name: Zewen Chi
paper_authors._id: 682d69ed056cf7b86cd8d6f1
paper_authors.name: Li Dong
paper_authors._id: 682d69ed056cf7b86cd8d6f2
paper_authors.name: Xingxing Zhang
paper_authors._id: 682d69ed056cf7b86cd8d6f3
paper_authors.name: Tengchao Lv
paper_authors._id: 682d69ed056cf7b86cd8d6f4
paper_authors.name: Lei Cui
paper_authors._id: 682d69ed056cf7b86cd8d6f5
paper_authors.name: Furu Wei
paper_title: Think Only When You Need with Large Hybrid-Reasoning Models
paper_summary: Recent Large Reasoning Models (LRMs) have shown substantially improved
  reasoning capabilities over traditional Large Language Models (LLMs) by
  incorporating extended thinking processes prior to producing final responses.
  However, excessively lengthy thinking introduces substantial overhead in terms
  of token consumption and latency, which is particularly unnecessary for simple
  queries. In this work, we introduce Large Hybrid-Reasoning Models (LHRMs), the
  first kind of model capable of adaptively determining whether to perform
  thinking based on the contextual information of user queries. To achieve this,
  we propose a two-stage training pipeline comprising Hybrid Fine-Tuning (HFT) as
  a cold start, followed by online reinforcement learning with the proposed
  Hybrid Group Policy Optimization (HGPO) to implicitly learn to select the
  appropriate thinking mode. Furthermore, we introduce a metric called Hybrid
  Accuracy to quantitatively assess the model's capability for hybrid thinking.
  Extensive experimental results show that LHRMs can adaptively perform hybrid
  thinking on queries of varying difficulty and type. It outperforms existing
  LRMs and LLMs in reasoning and general capabilities while significantly
  improving efficiency. Together, our work advocates for a reconsideration of the
  appropriate use of extended thinking processes and provides a solid starting
  point for building hybrid thinking systems.
paper_ai_summary: Large Hybrid-Reasoning Models dynamically choose between thinking modes based on query context, improving reasoning and efficiency compared to existing models.
paper_ai_keywords: Large Reasoning Models Large Language Models Hybrid Fine-Tuning Hybrid Group Policy Optimization Hybrid Accuracy hybrid thinking