title: Reasoning Models Can Be Effective Without Thinking
summary: Recent LLMs have significantly improved reasoning capabilities, primarily by
  including an explicit, lengthy Thinking process as part of generation. In this
  paper, we question whether this explicit thinking is necessary. Using the
  state-of-the-art DeepSeek-R1-Distill-Qwen, we find that bypassing the thinking
  process via simple prompting, denoted as NoThinking, can be surprisingly
  effective. When controlling for the number of tokens, NoThinking outperforms
  Thinking across a diverse set of seven challenging reasoning
  datasets--including mathematical problem solving, formal theorem proving, and
  coding--especially in low-budget settings, e.g., 51.3 vs. 28.9 on ACM 23 with
  700 tokens. Notably, the performance of NoThinking becomes more competitive
  with pass@k as k increases. Building on this observation, we demonstrate that a
  parallel scaling approach that uses NoThinking to generate N outputs
  independently and aggregates them is highly effective. For aggregation, we use
  task-specific verifiers when available, or we apply simple best-of-N strategies
  such as confidence-based selection. Our method outperforms a range of baselines
  with similar latency using Thinking, and is comparable to Thinking with
  significantly longer latency (up to 9x). Together, our research encourages a
  reconsideration of the necessity of lengthy thinking processes, while also
  establishing a competitive reference for achieving strong reasoning performance
  in low-budget settings or at low latency using parallel scaling.
paper_authors._id: 67fe895897acbf357c1f9725
paper_authors.name: Wenjie Ma
paper_authors._id: 67fe895897acbf357c1f9726
paper_authors.name: Jingxuan He
paper_authors._id: 67fe895897acbf357c1f9727
paper_authors.name: Charlie Snell
paper_authors._id: 67fe895897acbf357c1f9728
paper_authors.name: Tyler Griggs
paper_authors._id: 67fe895897acbf357c1f9729
paper_authors.name: Sewon Min
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-15T16:46:49.689Z
paper_authors._id: 63a76d0de27a6dbd485fe863
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63a76d0de27a6dbd485fe863/qJJwHOuvyQGq1o0KscOF_.jpeg
paper_authors.fullname: Sewon Min
paper_authors.type: user
paper_authors.user: sewon
paper_authors._id: 67fe895897acbf357c1f972a
paper_authors.name: Matei Zaharia
paper_title: Reasoning Models Can Be Effective Without Thinking
paper_summary: Recent LLMs have significantly improved reasoning capabilities, primarily by
  including an explicit, lengthy Thinking process as part of generation. In this
  paper, we question whether this explicit thinking is necessary. Using the
  state-of-the-art DeepSeek-R1-Distill-Qwen, we find that bypassing the thinking
  process via simple prompting, denoted as NoThinking, can be surprisingly
  effective. When controlling for the number of tokens, NoThinking outperforms
  Thinking across a diverse set of seven challenging reasoning
  datasets--including mathematical problem solving, formal theorem proving, and
  coding--especially in low-budget settings, e.g., 51.3 vs. 28.9 on ACM 23 with
  700 tokens. Notably, the performance of NoThinking becomes more competitive
  with pass@k as k increases. Building on this observation, we demonstrate that a
  parallel scaling approach that uses NoThinking to generate N outputs
  independently and aggregates them is highly effective. For aggregation, we use
  task-specific verifiers when available, or we apply simple best-of-N strategies
  such as confidence-based selection. Our method outperforms a range of baselines
  with similar latency using Thinking, and is comparable to Thinking with
  significantly longer latency (up to 9x). Together, our research encourages a
  reconsideration of the necessity of lengthy thinking processes, while also
  establishing a competitive reference for achieving strong reasoning performance
  in low-budget settings or at low latency using parallel scaling.
paper_ai_summary: Bypassing the explicit thinking process in large language models via simple prompting can lead to effective reasoning performance, especially in low-budget settings, and parallel scaling with this approach matches or surpasses models using lengthy thinking processes.
paper_ai_keywords: Thinking process NoThinking DeepSeek-R1-Distill-Qwen reasoning datasets mathematical problem solving formal theorem proving coding ACM 23 pass@k parallel scaling task-specific verifiers best-of-N strategies confidence-based selection