title: Thinkless: LLM Learns When to Think
summary: Reasoning Language Models, capable of extended chain-of-thought reasoning,
  have demonstrated remarkable performance on tasks requiring complex logical
  inference. However, applying elaborate reasoning for all queries often results
  in substantial computational inefficiencies, particularly when many problems
  admit straightforward solutions. This motivates an open question: Can LLMs
  learn when to think? To answer this, we propose Thinkless, a learnable
  framework that empowers an LLM to adaptively select between short-form and
  long-form reasoning, based on both task complexity and the model's ability.
  Thinkless is trained under a reinforcement learning paradigm and employs two
  control tokens, <short> for concise responses and <think> for detailed
  reasoning. At the core of our method is a Decoupled Group Relative Policy
  Optimization (DeGRPO) algorithm, which decomposes the learning objective of
  hybrid reasoning into two components: (1) a control token loss that governs the
  selection of the reasoning mode, and (2) a response loss that improves the
  accuracy of the generated answers. This decoupled formulation enables
  fine-grained control over the contributions of each objective, stabilizing
  training and effectively preventing collapse observed in vanilla GRPO.
  Empirically, on several benchmarks such as Minerva Algebra, MATH-500, and
  GSM8K, Thinkless is able to reduce the usage of long-chain thinking by 50% -
  90%, significantly improving the efficiency of Reasoning Language Models. The
  code is available at https://github.com/VainF/Thinkless
paper_authors._id: 682bf32f09ce6055262b42ec
paper_authors.name: Gongfan Fang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-20T07:20:41.314Z
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paper_authors.fullname: Gongfan Fang
paper_authors.type: user
paper_authors.user: Vinnnf
paper_authors._id: 682bf32f09ce6055262b42ed
paper_authors.name: Xinyin Ma
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-20T08:26:33.691Z
paper_authors._id: 64396ebc21221ac7411852b3
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paper_authors.fullname: Xinyin Ma
paper_authors.type: user
paper_authors.user: horseee
paper_authors._id: 682bf32f09ce6055262b42ee
paper_authors.name: Xinchao Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-20T08:27:00.912Z
paper_authors._id: 63fc03a50aab060792ffef39
paper_authors.avatarUrl: /avatars/9d5b1bb2a41928e08176b703935133ab.svg
paper_authors.fullname: Wangxinchao
paper_authors.type: user
paper_authors.user: wxcTest
paper_title: Thinkless: LLM Learns When to Think
paper_summary: Reasoning Language Models, capable of extended chain-of-thought reasoning,
  have demonstrated remarkable performance on tasks requiring complex logical
  inference. However, applying elaborate reasoning for all queries often results
  in substantial computational inefficiencies, particularly when many problems
  admit straightforward solutions. This motivates an open question: Can LLMs
  learn when to think? To answer this, we propose Thinkless, a learnable
  framework that empowers an LLM to adaptively select between short-form and
  long-form reasoning, based on both task complexity and the model's ability.
  Thinkless is trained under a reinforcement learning paradigm and employs two
  control tokens, <short> for concise responses and <think> for detailed
  reasoning. At the core of our method is a Decoupled Group Relative Policy
  Optimization (DeGRPO) algorithm, which decomposes the learning objective of
  hybrid reasoning into two components: (1) a control token loss that governs the
  selection of the reasoning mode, and (2) a response loss that improves the
  accuracy of the generated answers. This decoupled formulation enables
  fine-grained control over the contributions of each objective, stabilizing
  training and effectively preventing collapse observed in vanilla GRPO.
  Empirically, on several benchmarks such as Minerva Algebra, MATH-500, and
  GSM8K, Thinkless is able to reduce the usage of long-chain thinking by 50% -
  90%, significantly improving the efficiency of Reasoning Language Models. The
  code is available at https://github.com/VainF/Thinkless
paper_ai_summary: Thinkless enables LLMs to adaptively choose between short and long reasoning by using control tokens, reducing computational inefficiencies on benchmarks.
paper_ai_keywords: Reasoning Language Models extended chain-of-thought reasoning computational inefficiencies Thinkless reinforcement learning control tokens Decoupled Group Relative Policy Optimization DeGRPO control token loss response loss Minerva Algebra MATH-500 GSM8K