Think Only When You Need with Large Hybrid-Reasoning Models
Authors: name: Lingjie Jiang, name: Xun Wu, name: Shaohan Huang, name: Qingxiu Dong, name: Zewen Chi, name: Li Dong, name: Xingxing Zhang, name: Tengchao Lv, name: Lei Cui, name: Furu Wei

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.