title: Think Twice: Enhancing LLM Reasoning by Scaling Multi-round Test-time
    Thinking
summary: Recent advances in large language models (LLMs), such as OpenAI-o1 and
  DeepSeek-R1, have demonstrated the effectiveness of test-time scaling, where
  extended reasoning processes substantially enhance model performance. Despite
  this, current models are constrained by limitations in handling long texts and
  reinforcement learning (RL) training efficiency. To address these issues, we
  propose a simple yet effective test-time scaling approach Multi-round Thinking.
  This method iteratively refines model reasoning by leveraging previous answers
  as prompts for subsequent rounds. Extensive experiments across multiple models,
  including QwQ-32B and DeepSeek-R1, consistently show performance improvements
  on various benchmarks such as AIME 2024, MATH-500, GPQA-diamond, and
  LiveCodeBench. For instance, the accuracy of QwQ-32B improved from 80.3% (Round
  1) to 82.1% (Round 2) on the AIME 2024 dataset, while DeepSeek-R1 showed a
  similar increase from 79.7% to 82.0%. These results confirm that Multi-round
  Thinking is a broadly applicable, straightforward approach to achieving stable
  enhancements in model performance, underscoring its potential for future
  developments in test-time scaling techniques. The key prompt: {Original
  question prompt} The assistant's previous answer is: <answer> {last round
  answer} </answer>, and please re-answer.
paper_authors._id: 67e36792a281c900d76a93c8
paper_authors.name: Xiaoyu Tian
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-14T07:36:58.177Z
paper_authors._id: 621499d72be42a56cca7afad
paper_authors.avatarUrl: /avatars/3cea70d1a55c8096b4270093c69e4a5e.svg
paper_authors.fullname: TianXiaoyu
paper_authors.type: user
paper_authors.user: Emperorizzis
paper_authors._id: 67e36792a281c900d76a93c9
paper_authors.name: Sitong Zhao
paper_authors._id: 67e36792a281c900d76a93ca
paper_authors.name: Haotian Wang
paper_authors._id: 67e36792a281c900d76a93cb
paper_authors.name: Shuaiting Chen
paper_authors._id: 67e36792a281c900d76a93cc
paper_authors.name: Yunjie Ji
paper_authors._id: 67e36792a281c900d76a93cd
paper_authors.name: Yiping Peng
paper_authors._id: 67e36792a281c900d76a93ce
paper_authors.name: Han Zhao
paper_authors._id: 67e36792a281c900d76a93cf
paper_authors.name: Xiangang Li
paper_title: Think Twice: Enhancing LLM Reasoning by Scaling Multi-round Test-time
    Thinking
paper_summary: Recent advances in large language models (LLMs), such as OpenAI-o1 and
  DeepSeek-R1, have demonstrated the effectiveness of test-time scaling, where
  extended reasoning processes substantially enhance model performance. Despite
  this, current models are constrained by limitations in handling long texts and
  reinforcement learning (RL) training efficiency. To address these issues, we
  propose a simple yet effective test-time scaling approach Multi-round Thinking.
  This method iteratively refines model reasoning by leveraging previous answers
  as prompts for subsequent rounds. Extensive experiments across multiple models,
  including QwQ-32B and DeepSeek-R1, consistently show performance improvements
  on various benchmarks such as AIME 2024, MATH-500, GPQA-diamond, and
  LiveCodeBench. For instance, the accuracy of QwQ-32B improved from 80.3% (Round
  1) to 82.1% (Round 2) on the AIME 2024 dataset, while DeepSeek-R1 showed a
  similar increase from 79.7% to 82.0%. These results confirm that Multi-round
  Thinking is a broadly applicable, straightforward approach to achieving stable
  enhancements in model performance, underscoring its potential for future
  developments in test-time scaling techniques. The key prompt: {Original
  question prompt} The assistant's previous answer is: <answer> {last round
  answer} </answer>, and please re-answer.
paper_ai_summary: A multi-round thinking approach at test time improves the performance of large language models by iteratively refining their reasoning processes, enhancing accuracy on various benchmarks.
paper_ai_keywords: large language models test-time scaling reinforcement learning Multi-round Thinking QwQ-32B DeepSeek-R1 AIME 2024 MATH-500 GPQA-diamond LiveCodeBench