title: Done Is Better than Perfect: Unlocking Efficient Reasoning by Structured
    Multi-Turn Decomposition
summary: Large Reasoning Models (LRMs) are criticized for the excessively lengthy
  Chain-of-Thought (CoT) to derive the final answer, suffering from high
  first-token and overall latency. Typically, the CoT of LRMs mixes multiple
  thinking units; each unit attempts to produce a candidate answer to the
  original query. Hence, a natural idea to improve efficiency is to reduce the
  unit number. Yet, the fact that the thinking units in vanilla CoT cannot be
  explicitly managed renders doing so challenging. This paper introduces
  Multi-Turn Decomposition (MinD) to decode conventional CoT into a sequence of
  explicit, structured, and turn-wise interactions to bridge the gap. In MinD,
  the model provides a multi-turn response to the query, where each turn embraces
  a thinking unit and yields a corresponding answer. The subsequent turns can
  reflect, verify, revise, or explore alternative approaches to both the thinking
  and answer parts of earlier ones. This not only makes the answer delivered more
  swiftly, but also enables explicit controls over the iterative reasoning
  process (i.e., users may halt or continue at any turn). We follow a supervised
  fine-tuning (SFT) then reinforcement learning (RL) paradigm to realize MinD. We
  first rephrase the outputs of an LRM into multi-turn formats by prompting
  another LLM, and then tune the LRM with such data. Observing that the tuned
  model tends to consume even more tokens than the original one (probably due to
  that the multi-turn formats introduce additional answer tokens), we advocate
  leveraging RL algorithms like GRPO to prioritize correct outputs with fewer
  turns. Trained on the MATH dataset using R1-Distill models, MinD can achieve up
  to ~70% reduction in both output token usage and time to first token (TTFT),
  while maintaining competitive performance on reasoning benchmarks such as
  MATH-500, AIME24, AMC23, and GPQA-Diamond.
paper_authors._id: 68352630363d6fd2fff5d07f
paper_authors.name: Zihao Zeng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-29T07:44:09.133Z
paper_authors._id: 6544c0585a13979f82038a1c
paper_authors.avatarUrl: /avatars/01f3e862d49020e9eaf1728e4ba97bea.svg
paper_authors.fullname: Zeng Zihao
paper_authors.type: user
paper_authors.user: zzh6666
paper_authors._id: 68352630363d6fd2fff5d080
paper_authors.name: Xuyao Huang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-27T07:51:44.216Z
paper_authors._id: 6721dacfc5309c08451d21d5
paper_authors.avatarUrl: /avatars/ac8be5ac8b8ee5b5533214e526b72dad.svg
paper_authors.fullname: Huang Xuyao
paper_authors.type: user
paper_authors.user: ElysiaTrue
paper_authors._id: 68352630363d6fd2fff5d081
paper_authors.name: Boxiu Li
paper_authors._id: 68352630363d6fd2fff5d082
paper_authors.name: Hao Zhang
paper_authors._id: 68352630363d6fd2fff5d083
paper_authors.name: Zhijie Deng
paper_title: Done Is Better than Perfect: Unlocking Efficient Reasoning by Structured
    Multi-Turn Decomposition
paper_summary: Large Reasoning Models (LRMs) are criticized for the excessively lengthy
  Chain-of-Thought (CoT) to derive the final answer, suffering from high
  first-token and overall latency. Typically, the CoT of LRMs mixes multiple
  thinking units; each unit attempts to produce a candidate answer to the
  original query. Hence, a natural idea to improve efficiency is to reduce the
  unit number. Yet, the fact that the thinking units in vanilla CoT cannot be
  explicitly managed renders doing so challenging. This paper introduces
  Multi-Turn Decomposition (MinD) to decode conventional CoT into a sequence of
  explicit, structured, and turn-wise interactions to bridge the gap. In MinD,
  the model provides a multi-turn response to the query, where each turn embraces
  a thinking unit and yields a corresponding answer. The subsequent turns can
  reflect, verify, revise, or explore alternative approaches to both the thinking
  and answer parts of earlier ones. This not only makes the answer delivered more
  swiftly, but also enables explicit controls over the iterative reasoning
  process (i.e., users may halt or continue at any turn). We follow a supervised
  fine-tuning (SFT) then reinforcement learning (RL) paradigm to realize MinD. We
  first rephrase the outputs of an LRM into multi-turn formats by prompting
  another LLM, and then tune the LRM with such data. Observing that the tuned
  model tends to consume even more tokens than the original one (probably due to
  that the multi-turn formats introduce additional answer tokens), we advocate
  leveraging RL algorithms like GRPO to prioritize correct outputs with fewer
  turns. Trained on the MATH dataset using R1-Distill models, MinD can achieve up
  to ~70% reduction in both output token usage and time to first token (TTFT),
  while maintaining competitive performance on reasoning benchmarks such as
  MATH-500, AIME24, AMC23, and GPQA-Diamond.
paper_ai_summary: Multi-Turn Decomposition improves efficiency in large reasoning models by breaking down chain-of-thought into manageable turns, reducing token usage and latency while maintaining performance.
paper_ai_keywords: Chain-of-Thought large reasoning models multi-turn decomposition thinking units iterative reasoning process supervised fine-tuning reinforcement learning MATH dataset R1-Distill models MATH-500 AIME24 AMC23 GPQA-Diamond