title: ARM: Adaptive Reasoning Model
summary: While large reasoning models demonstrate strong performance on complex tasks,
  they lack the ability to adjust reasoning token usage based on task difficulty.
  This often leads to the "overthinking" problem -- excessive and unnecessary
  reasoning -- which, although potentially mitigated by human intervention to
  control the token budget, still fundamentally contradicts the goal of achieving
  fully autonomous AI. In this work, we propose Adaptive Reasoning Model (ARM), a
  reasoning model capable of adaptively selecting appropriate reasoning formats
  based on the task at hand. These formats include three efficient ones -- Direct
  Answer, Short CoT, and Code -- as well as a more elaborate format, Long CoT. To
  train ARM, we introduce Ada-GRPO, an adaptation of Group Relative Policy
  Optimization (GRPO), which addresses the format collapse issue in traditional
  GRPO. Ada-GRPO enables ARM to achieve high token efficiency, reducing tokens by
  an average of 30%, and up to 70%, while maintaining performance comparable to
  the model that relies solely on Long CoT. Furthermore, not only does it improve
  inference efficiency through reduced token generation, but it also brings a 2x
  speedup in training. In addition to the default Adaptive Mode, ARM supports two
  additional reasoning modes: 1) Instruction-Guided Mode, which allows users to
  explicitly specify the reasoning format via special tokens -- ideal when the
  appropriate format is known for a batch of tasks. 2) Consensus-Guided Mode,
  which aggregates the outputs of the three efficient formats and resorts to Long
  CoT in case of disagreement, prioritizing performance with higher token usage.
paper_authors._id: 68352b5803548b71276c1a6f
paper_authors.name: Siye Wu
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paper_authors.fullname: Siye Wu
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paper_authors._id: 68352b5803548b71276c1a70
paper_authors.name: Jian Xie
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paper_authors.fullname: Jian Xie
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paper_authors.user: hsaest
paper_authors._id: 68352b5803548b71276c1a71
paper_authors.name: Yikai Zhang
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paper_authors.fullname: Yikai Zhang
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paper_authors.user: Arist12
paper_authors._id: 68352b5803548b71276c1a72
paper_authors.name: Aili Chen
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paper_authors.fullname: Ellie Chen
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paper_authors.user: sheep33333
paper_authors._id: 68352b5803548b71276c1a73
paper_authors.name: Kai Zhang
paper_authors._id: 68352b5803548b71276c1a74
paper_authors.name: Yu Su
paper_authors._id: 68352b5803548b71276c1a75
paper_authors.name: Yanghua Xiao
paper_title: ARM: Adaptive Reasoning Model
paper_summary: While large reasoning models demonstrate strong performance on complex tasks,
  they lack the ability to adjust reasoning token usage based on task difficulty.
  This often leads to the "overthinking" problem -- excessive and unnecessary
  reasoning -- which, although potentially mitigated by human intervention to
  control the token budget, still fundamentally contradicts the goal of achieving
  fully autonomous AI. In this work, we propose Adaptive Reasoning Model (ARM), a
  reasoning model capable of adaptively selecting appropriate reasoning formats
  based on the task at hand. These formats include three efficient ones -- Direct
  Answer, Short CoT, and Code -- as well as a more elaborate format, Long CoT. To
  train ARM, we introduce Ada-GRPO, an adaptation of Group Relative Policy
  Optimization (GRPO), which addresses the format collapse issue in traditional
  GRPO. Ada-GRPO enables ARM to achieve high token efficiency, reducing tokens by
  an average of 30%, and up to 70%, while maintaining performance comparable to
  the model that relies solely on Long CoT. Furthermore, not only does it improve
  inference efficiency through reduced token generation, but it also brings a 2x
  speedup in training. In addition to the default Adaptive Mode, ARM supports two
  additional reasoning modes: 1) Instruction-Guided Mode, which allows users to
  explicitly specify the reasoning format via special tokens -- ideal when the
  appropriate format is known for a batch of tasks. 2) Consensus-Guided Mode,
  which aggregates the outputs of the three efficient formats and resorts to Long
  CoT in case of disagreement, prioritizing performance with higher token usage.
paper_ai_summary: Adaptive Reasoning Model (ARM) uses Ada-GRPO to reduce token usage and improve efficiency across different reasoning modes.
paper_ai_keywords: Adaptive Reasoning Model ARM Ada-GRPO Group Relative Policy Optimization GRPO format collapse token efficiency inference efficiency Direct Answer Short CoT Code Long CoT Adaptive Mode Instruction-Guided Mode Consensus-Guided Mode