title: AdaptThink: Reasoning Models Can Learn When to Think
summary: Recently, large reasoning models have achieved impressive performance on
  various tasks by employing human-like deep thinking. However, the lengthy
  thinking process substantially increases inference overhead, making efficiency
  a critical bottleneck. In this work, we first demonstrate that NoThinking,
  which prompts the reasoning model to skip thinking and directly generate the
  final solution, is a better choice for relatively simple tasks in terms of both
  performance and efficiency. Motivated by this, we propose AdaptThink, a novel
  RL algorithm to teach reasoning models to choose the optimal thinking mode
  adaptively based on problem difficulty. Specifically, AdaptThink features two
  core components: (1) a constrained optimization objective that encourages the
  model to choose NoThinking while maintaining the overall performance; (2) an
  importance sampling strategy that balances Thinking and NoThinking samples
  during on-policy training, thereby enabling cold start and allowing the model
  to explore and exploit both thinking modes throughout the training process. Our
  experiments indicate that AdaptThink significantly reduces the inference costs
  while further enhancing performance. Notably, on three math datasets,
  AdaptThink reduces the average response length of DeepSeek-R1-Distill-Qwen-1.5B
  by 53% and improves its accuracy by 2.4%, highlighting the promise of adaptive
  thinking-mode selection for optimizing the balance between reasoning quality
  and efficiency. Our codes and models are available at
  https://github.com/THU-KEG/AdaptThink.
paper_authors._id: 682be3e43ba4cfbca886a521
paper_authors.name: Jiajie Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-20T08:10:15.004Z
paper_authors._id: 66cdd285c51a915bd5f2d017
paper_authors.avatarUrl: /avatars/14e5794307e4672b1b51d26b31227e0f.svg
paper_authors.fullname: Jiajie Zhang
paper_authors.type: user
paper_authors.user: NeoZ123
paper_authors._id: 682be3e43ba4cfbca886a522
paper_authors.name: Nianyi Lin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-20T08:10:51.330Z
paper_authors._id: 67385497d9af4eb4c078ced3
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/yP-EPaY0tUosVR4kjXQ9B.png
paper_authors.fullname: Lin Nianyi
paper_authors.type: user
paper_authors.user: linny2002
paper_authors._id: 682be3e43ba4cfbca886a523
paper_authors.name: Lei Hou
paper_authors._id: 682be3e43ba4cfbca886a524
paper_authors.name: Ling Feng
paper_authors._id: 682be3e43ba4cfbca886a525
paper_authors.name: Juanzi Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-20T08:10:58.673Z
paper_authors._id: 65df8cbc2705d9672f55d1aa
paper_authors.avatarUrl: /avatars/63e46f15bb76bd9d4508fd0f54f39829.svg
paper_authors.fullname: Juanzi Li
paper_authors.type: user
paper_authors.user: juanli
paper_title: AdaptThink: Reasoning Models Can Learn When to Think
paper_summary: Recently, large reasoning models have achieved impressive performance on
  various tasks by employing human-like deep thinking. However, the lengthy
  thinking process substantially increases inference overhead, making efficiency
  a critical bottleneck. In this work, we first demonstrate that NoThinking,
  which prompts the reasoning model to skip thinking and directly generate the
  final solution, is a better choice for relatively simple tasks in terms of both
  performance and efficiency. Motivated by this, we propose AdaptThink, a novel
  RL algorithm to teach reasoning models to choose the optimal thinking mode
  adaptively based on problem difficulty. Specifically, AdaptThink features two
  core components: (1) a constrained optimization objective that encourages the
  model to choose NoThinking while maintaining the overall performance; (2) an
  importance sampling strategy that balances Thinking and NoThinking samples
  during on-policy training, thereby enabling cold start and allowing the model
  to explore and exploit both thinking modes throughout the training process. Our
  experiments indicate that AdaptThink significantly reduces the inference costs
  while further enhancing performance. Notably, on three math datasets,
  AdaptThink reduces the average response length of DeepSeek-R1-Distill-Qwen-1.5B
  by 53% and improves its accuracy by 2.4%, highlighting the promise of adaptive
  thinking-mode selection for optimizing the balance between reasoning quality
  and efficiency. Our codes and models are available at
  https://github.com/THU-KEG/AdaptThink.
paper_ai_summary: AdaptThink, an RL algorithm, enhances reasoning models' efficiency and performance by adaptively selecting the optimal thinking mode based on problem difficulty.
paper_ai_keywords: NoThinking Reinforcement Learning (RL) constrained optimization objective importance sampling strategy on-policy training DeepSeek-R1-Distill-Qwen-1.5B reasoning quality efficiency