First Try Matters: Revisiting the Role of Reflection in Reasoning Models
Large language models have recently demonstrated significant gains in
reasoning ability, often attributed to their capacity to generate longer chains
of thought and engage in reflective reasoning. However, the contribution of
reflections to performance improvement remains unclear. In this paper, we
systematically analyze the rollouts of eight reasoning models on five
mathematical datasets. We focus on reflective behaviours where the model has
already produced an answer but continues reflecting before finalizing its
output. Our analysis reveals that reflections are predominantly confirmatory
and rarely alter the model's initial answer, a pattern consistent across models
and datasets. To understand the role of reflections in training, we construct
supervised fine-tuning (SFT) datasets with varying amounts of reflection steps.
We observe that training models on rollouts with more reflection steps
primarily enhances first-answer correctness rather than the ability to correct
initially wrong answers through reflections. This motivates us to propose a
question-aware early-stopping method that enhances inference-time token
efficiency by stopping the reasoning process once a few plausible candidate
answers are generated, thereby reducing unnecessary reflection steps. Motivated
by this, we further propose to dynamically truncate the reflections after a
candidate answer has appeared during generation, which reduces reasoning tokens
by 24.5% across five mathematical datasets, within a 2.9% drop in accuracy.
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Liwei Kang
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2025-10-16T07:25:44.987Z
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Liwei Kang
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Yue Deng
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Yue Deng
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spencer97
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Yao Xiao
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2025-10-16T07:27:08.233Z
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 Xiao Yao
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Zhanfeng Mo
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Zhanfeng Mo
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mzf666
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Wee Sun Lee
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Lidong Bing
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2025-10-16T07:25:46.716Z
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Lidong Bing
user
LidongBing
First Try Matters: Revisiting the Role of Reflection in Reasoning Models
Large language models have recently demonstrated significant gains in
reasoning ability, often attributed to their capacity to generate longer chains
of thought and engage in reflective reasoning. However, the contribution of
reflections to performance improvement remains unclear. In this paper, we
systematically analyze the rollouts of eight reasoning models on five
mathematical datasets. We focus on reflective behaviours where the model has
already produced an answer but continues reflecting before finalizing its
output. Our analysis reveals that reflections are predominantly confirmatory
and rarely alter the model's initial answer, a pattern consistent across models
and datasets. To understand the role of reflections in training, we construct
supervised fine-tuning (SFT) datasets with varying amounts of reflection steps.
We observe that training models on rollouts with more reflection steps
primarily enhances first-answer correctness rather than the ability to correct
initially wrong answers through reflections. This motivates us to propose a
question-aware early-stopping method that enhances inference-time token
efficiency by stopping the reasoning process once a few plausible candidate
answers are generated, thereby reducing unnecessary reflection steps. Motivated
by this, we further propose to dynamically truncate the reflections after a
candidate answer has appeared during generation, which reduces reasoning tokens
by 24.5% across five mathematical datasets, within a 2.9% drop in accuracy.
Analysis of reflective behaviors in reasoning models shows that reflections primarily confirm initial answers, and training with more reflections improves first-answer correctness; a question-aware early-stopping method reduces unnecessary reflections and tokens with minimal accuracy loss.
reasoning models reflective behaviors confirmatory reflections supervised fine-tuning early-stopping method token efficiency reasoning tokens candidate answers