Done Is Better than Perfect: Unlocking Efficient Reasoning by Structured
  Multi-Turn Decomposition
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.
68352630363d6fd2fff5d07f
Zihao Zeng
claimed_verified
2025-05-29T07:44:09.133Z
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/avatars/01f3e862d49020e9eaf1728e4ba97bea.svg
Zeng Zihao
user
zzh6666
68352630363d6fd2fff5d080
Xuyao Huang
claimed_verified
2025-05-27T07:51:44.216Z
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/avatars/ac8be5ac8b8ee5b5533214e526b72dad.svg
Huang Xuyao
user
ElysiaTrue
68352630363d6fd2fff5d081
Boxiu Li
68352630363d6fd2fff5d082
Hao Zhang
68352630363d6fd2fff5d083
Zhijie Deng
Done Is Better than Perfect: Unlocking Efficient Reasoning by Structured
  Multi-Turn Decomposition
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.
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.
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