title: AdaR1: From Long-CoT to Hybrid-CoT via Bi-Level Adaptive Reasoning
    Optimization
summary: Recently, long-thought reasoning models achieve strong performance on complex
  reasoning tasks, but often incur substantial inference overhead, making
  efficiency a critical concern. Our empirical analysis reveals that the benefit
  of using Long-CoT varies across problems: while some problems require elaborate
  reasoning, others show no improvement, or even degraded accuracy. This
  motivates adaptive reasoning strategies that tailor reasoning depth to the
  input. However, prior work primarily reduces redundancy within long reasoning
  paths, limiting exploration of more efficient strategies beyond the Long-CoT
  paradigm. To address this, we propose a novel two-stage framework for adaptive
  and efficient reasoning. First, we construct a hybrid reasoning model by
  merging long and short CoT models to enable diverse reasoning styles. Second,
  we apply bi-level preference training to guide the model to select suitable
  reasoning styles (group-level), and prefer concise and correct reasoning within
  each style group (instance-level). Experiments demonstrate that our method
  significantly reduces inference costs compared to other baseline approaches,
  while maintaining performance. Notably, on five mathematical datasets, the
  average length of reasoning is reduced by more than 50%, highlighting the
  potential of adaptive strategies to optimize reasoning efficiency in large
  language models. Our code is coming soon at https://github.com/StarDewXXX/AdaR1
paper_authors._id: 68142de6111ccf18a993c890
paper_authors.name: Haotian Luo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-05T07:46:53.110Z
paper_authors._id: 632ab8f5a968c34257da5c52
paper_authors.avatarUrl: /avatars/59df09e6c9e1e633170514d950ad7981.svg
paper_authors.fullname: Haotian Luo
paper_authors.type: user
paper_authors.user: LordNoah
paper_authors._id: 68142de6111ccf18a993c891
paper_authors.name: Haiying He
paper_authors._id: 68142de6111ccf18a993c892
paper_authors.name: Yibo Wang
paper_authors._id: 68142de6111ccf18a993c893
paper_authors.name: Jinluan Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-04T10:05:16.431Z
paper_authors._id: 6592472fccbc1e2cc7250903
paper_authors.avatarUrl: /avatars/6f04ae66944eb2ce65c5aca7927bab10.svg
paper_authors.fullname: Jinluan Yang
paper_authors.type: user
paper_authors.user: Jinluan
paper_authors._id: 68142de6111ccf18a993c894
paper_authors.name: Rui Liu
paper_authors._id: 68142de6111ccf18a993c895
paper_authors.name: Naiqiang Tan
paper_authors._id: 68142de6111ccf18a993c896
paper_authors.name: Xiaochun Cao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-05T07:47:51.395Z
paper_authors._id: 64ddc5ea83645d0f98599d6e
paper_authors.avatarUrl: /avatars/99a148c29cfbff77ed6150cc279a47c0.svg
paper_authors.fullname: caoxiaochun
paper_authors.type: user
paper_authors.user: cxc361461518
paper_authors._id: 68142de6111ccf18a993c897
paper_authors.name: Dacheng Tao
paper_authors._id: 68142de6111ccf18a993c898
paper_authors.name: Li Shen
paper_title: AdaR1: From Long-CoT to Hybrid-CoT via Bi-Level Adaptive Reasoning
    Optimization
paper_summary: Recently, long-thought reasoning models achieve strong performance on complex
  reasoning tasks, but often incur substantial inference overhead, making
  efficiency a critical concern. Our empirical analysis reveals that the benefit
  of using Long-CoT varies across problems: while some problems require elaborate
  reasoning, others show no improvement, or even degraded accuracy. This
  motivates adaptive reasoning strategies that tailor reasoning depth to the
  input. However, prior work primarily reduces redundancy within long reasoning
  paths, limiting exploration of more efficient strategies beyond the Long-CoT
  paradigm. To address this, we propose a novel two-stage framework for adaptive
  and efficient reasoning. First, we construct a hybrid reasoning model by
  merging long and short CoT models to enable diverse reasoning styles. Second,
  we apply bi-level preference training to guide the model to select suitable
  reasoning styles (group-level), and prefer concise and correct reasoning within
  each style group (instance-level). Experiments demonstrate that our method
  significantly reduces inference costs compared to other baseline approaches,
  while maintaining performance. Notably, on five mathematical datasets, the
  average length of reasoning is reduced by more than 50%, highlighting the
  potential of adaptive strategies to optimize reasoning efficiency in large
  language models. Our code is coming soon at https://github.com/StarDewXXX/AdaR1
paper_ai_summary: A two-stage adaptive reasoning framework combining long and short chain-of-thought models, trained with bi-level preference learning, reduces inference costs while maintaining performance across mathematical datasets.
paper_ai_keywords: long chain-of-thought models short chain-of-thought models hybrid reasoning model bi-level preference training adaptive reasoning strategies inference costs mathematical datasets