title: s1: Simple test-time scaling
summary: Test-time scaling is a promising new approach to language modeling that uses
  extra test-time compute to improve performance. Recently, OpenAI's o1 model
  showed this capability but did not publicly share its methodology, leading to
  many replication efforts. We seek the simplest approach to achieve test-time
  scaling and strong reasoning performance. First, we curate a small dataset s1K
  of 1,000 questions paired with reasoning traces relying on three criteria we
  validate through ablations: difficulty, diversity, and quality. Second, we
  develop budget forcing to control test-time compute by forcefully terminating
  the model's thinking process or lengthening it by appending "Wait" multiple
  times to the model's generation when it tries to end. This can lead the model
  to double-check its answer, often fixing incorrect reasoning steps. After
  supervised finetuning the Qwen2.5-32B-Instruct language model on s1K and
  equipping it with budget forcing, our model s1 exceeds o1-preview on
  competition math questions by up to 27% (MATH and AIME24). Further, scaling s1
  with budget forcing allows extrapolating beyond its performance without
  test-time intervention: from 50% to 57% on AIME24. Our model, data, and code
  are open-source at https://github.com/simplescaling/s1.
paper_authors._id: 67a02dd80e751b0476a1bcc6
paper_authors.name: Niklas Muennighoff
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-03T11:07:41.451Z
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paper_authors.fullname: Niklas Muennighoff
paper_authors.type: user
paper_authors.user: Muennighoff
paper_authors._id: 67a02dd80e751b0476a1bcc7
paper_authors.name: Zitong Yang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-03T11:09:05.373Z
paper_authors._id: 65a5b721f6cfc4b24a75732b
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paper_authors.fullname: Zitong Yang
paper_authors.type: user
paper_authors.user: zitongyang
paper_authors._id: 67a02dd80e751b0476a1bcc8
paper_authors.name: Weijia Shi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-03T11:09:20.064Z
paper_authors._id: 6400f2ed568dbe30c9161e47
paper_authors.avatarUrl: /avatars/c55938df5bce82b5d96e592a1ec36a8b.svg
paper_authors.fullname: Weijia Shi
paper_authors.type: user
paper_authors.user: swj0419
paper_authors._id: 67a02dd80e751b0476a1bcc9
paper_authors.name: Xiang Lisa Li
paper_authors._id: 67a02dd80e751b0476a1bcca
paper_authors.name: Li Fei-Fei
paper_authors._id: 67a02dd80e751b0476a1bccb
paper_authors.name: Hannaneh Hajishirzi
paper_authors._id: 67a02dd80e751b0476a1bccc
paper_authors.name: Luke Zettlemoyer
paper_authors._id: 67a02dd80e751b0476a1bccd
paper_authors.name: Percy Liang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-03T11:10:17.521Z
paper_authors._id: 6409651b9e9f790c905b2335
paper_authors.avatarUrl: /avatars/1fb8c80b60f21f65a0a027319101f236.svg
paper_authors.fullname: Percy Liang
paper_authors.type: user
paper_authors.user: percyliang
paper_authors._id: 67a02dd80e751b0476a1bcce
paper_authors.name: Emmanuel Candès
paper_authors._id: 67a02dd80e751b0476a1bccf
paper_authors.name: Tatsunori Hashimoto
paper_title: s1: Simple test-time scaling
paper_summary: Test-time scaling is a promising new approach to language modeling that uses
  extra test-time compute to improve performance. Recently, OpenAI's o1 model
  showed this capability but did not publicly share its methodology, leading to
  many replication efforts. We seek the simplest approach to achieve test-time
  scaling and strong reasoning performance. First, we curate a small dataset s1K
  of 1,000 questions paired with reasoning traces relying on three criteria we
  validate through ablations: difficulty, diversity, and quality. Second, we
  develop budget forcing to control test-time compute by forcefully terminating
  the model's thinking process or lengthening it by appending "Wait" multiple
  times to the model's generation when it tries to end. This can lead the model
  to double-check its answer, often fixing incorrect reasoning steps. After
  supervised finetuning the Qwen2.5-32B-Instruct language model on s1K and
  equipping it with budget forcing, our model s1 exceeds o1-preview on
  competition math questions by up to 27% (MATH and AIME24). Further, scaling s1
  with budget forcing allows extrapolating beyond its performance without
  test-time intervention: from 50% to 57% on AIME24. Our model, data, and code
  are open-source at https://github.com/simplescaling/s1.
paper_ai_keywords: test-time scaling language modeling test-time compute recognition traces budget forcing model's thinking process supervised finetuning Qwen2.5-32B-Instruct embedding math questions MATH AIME24 extrapolating performance