AdaR1: From Long-CoT to Hybrid-CoT via Bi-Level Adaptive Reasoning
  Optimization
Authors: name: Haotian Luo, name: Haiying He, name: Yibo Wang, name: Jinluan Yang, name: Rui Liu, name: Naiqiang Tan, name: Xiaochun Cao, name: Dacheng Tao, name: Li Shen

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