Think Twice: Enhancing LLM Reasoning by Scaling Multi-round Test-time
  Thinking
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.
67e36792a281c900d76a93c8
Xiaoyu Tian
claimed_verified
2025-05-14T07:36:58.177Z
621499d72be42a56cca7afad
/avatars/3cea70d1a55c8096b4270093c69e4a5e.svg
TianXiaoyu
user
Emperorizzis
67e36792a281c900d76a93c9
Sitong Zhao
67e36792a281c900d76a93ca
Haotian Wang
67e36792a281c900d76a93cb
Shuaiting Chen
67e36792a281c900d76a93cc
Yunjie Ji
67e36792a281c900d76a93cd
Yiping Peng
67e36792a281c900d76a93ce
Han Zhao
67e36792a281c900d76a93cf
Xiangang Li
Think Twice: Enhancing LLM Reasoning by Scaling Multi-round Test-time
  Thinking
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.
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.
large language models test-time scaling reinforcement learning Multi-round Thinking QwQ-32B DeepSeek-R1 AIME 2024 MATH-500 GPQA-diamond LiveCodeBench