Think Only When You Need with Large Hybrid-Reasoning Models
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.
682d69ed056cf7b86cd8d6ec
Lingjie Jiang
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2025-05-21T08:40:17.384Z
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Jack
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lingjie23
682d69ed056cf7b86cd8d6ed
Xun Wu
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2025-05-21T08:40:20.741Z
62d1227384bfbee86b6eec56
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Xun Wu
user
YUSHUIWX
682d69ed056cf7b86cd8d6ee
Shaohan Huang
682d69ed056cf7b86cd8d6ef
Qingxiu Dong
682d69ed056cf7b86cd8d6f0
Zewen Chi
682d69ed056cf7b86cd8d6f1
Li Dong
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2025-06-10T09:29:55.760Z
5df85abada6d0311fd3d5408
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Li Dong
user
unilm
682d69ed056cf7b86cd8d6f2
Xingxing Zhang
682d69ed056cf7b86cd8d6f3
Tengchao Lv
682d69ed056cf7b86cd8d6f4
Lei Cui
682d69ed056cf7b86cd8d6f5
Furu Wei
Think Only When You Need with Large Hybrid-Reasoning Models
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.
Large Hybrid-Reasoning Models dynamically choose between thinking modes based on query context, improving reasoning and efficiency compared to existing models.
Large Reasoning Models Large Language Models Hybrid Fine-Tuning Hybrid Group Policy Optimization Hybrid Accuracy hybrid thinking