AdaR1: From Long-CoT to Hybrid-CoT via Bi-Level Adaptive Reasoning
  Optimization
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
68142de6111ccf18a993c890
Haotian Luo
admin_assigned
2025-05-05T07:46:53.110Z
632ab8f5a968c34257da5c52
/avatars/59df09e6c9e1e633170514d950ad7981.svg
Haotian Luo
user
LordNoah
68142de6111ccf18a993c891
Haiying He
68142de6111ccf18a993c892
Yibo Wang
68142de6111ccf18a993c893
Jinluan Yang
claimed_verified
2025-05-04T10:05:16.431Z
6592472fccbc1e2cc7250903
/avatars/6f04ae66944eb2ce65c5aca7927bab10.svg
Jinluan Yang
user
Jinluan
68142de6111ccf18a993c894
Rui Liu
68142de6111ccf18a993c895
Naiqiang Tan
68142de6111ccf18a993c896
Xiaochun Cao
admin_assigned
2025-05-05T07:47:51.395Z
64ddc5ea83645d0f98599d6e
/avatars/99a148c29cfbff77ed6150cc279a47c0.svg
caoxiaochun
user
cxc361461518
68142de6111ccf18a993c897
Dacheng Tao
68142de6111ccf18a993c898
Li Shen
AdaR1: From Long-CoT to Hybrid-CoT via Bi-Level Adaptive Reasoning
  Optimization
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
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.
long chain-of-thought models short chain-of-thought models hybrid reasoning model bi-level preference training adaptive reasoning strategies inference costs mathematical datasets