AdaptiveStep: Automatically Dividing Reasoning Step through Model
  Confidence
Current approaches for training Process Reward Models (PRMs) often involve
breaking down responses into multiple reasoning steps using rule-based
techniques, such as using predefined placeholder tokens or setting the
reasoning step's length into a fixed size. These approaches overlook the fact
that specific words do not typically mark true decision points in a text. To
address this, we propose AdaptiveStep, a method that divides reasoning steps
based on the model's confidence in predicting the next word. This division
method provides more decision-making information at each step, enhancing
downstream tasks, such as reward model learning. Moreover, our method does not
require manual annotation. We demonstrate its effectiveness through experiments
with AdaptiveStep-trained PRMs in mathematical reasoning and code generation
tasks. Experimental results indicate that the outcome PRM achieves
state-of-the-art Best-of-N performance, surpassing greedy search strategy with
token-level value-guided decoding, while also reducing construction costs by
over 30% compared to existing open-source PRMs. In addition, we provide a
thorough analysis and case study on the PRM's performance, transferability, and
generalization capabilities.
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Yuliang Liu
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2025-02-20T17:11:40.282Z
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Yuliang Liu
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Junjie Lu
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Junjie Lu
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Lux0926
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Zhaoling Chen
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Chaofeng Qu
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2025-02-20T17:11:10.302Z
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Chaofeng QU
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kylebrovloski
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Jason Klein Liu
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2025-02-20T17:11:16.565Z
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Jason Klein Liu
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jasonkleinlove
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Chonghan Liu
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Zefan Cai
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Yunhui Xia
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Li Zhao
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Jiang Bian
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2025-02-20T17:00:08.549Z
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Jiang Bian
user
bianjiang
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Chuheng Zhang
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2025-02-20T16:59:49.312Z
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ZhangChuheng
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zhangchuheng123
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Wei Shen
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Zhouhan Lin
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2025-09-23T10:07:53.548Z
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Zhouhan Lin
user
ZhouhanLin
AdaptiveStep: Automatically Dividing Reasoning Step through Model
  Confidence
Current approaches for training Process Reward Models (PRMs) often involve
breaking down responses into multiple reasoning steps using rule-based
techniques, such as using predefined placeholder tokens or setting the
reasoning step's length into a fixed size. These approaches overlook the fact
that specific words do not typically mark true decision points in a text. To
address this, we propose AdaptiveStep, a method that divides reasoning steps
based on the model's confidence in predicting the next word. This division
method provides more decision-making information at each step, enhancing
downstream tasks, such as reward model learning. Moreover, our method does not
require manual annotation. We demonstrate its effectiveness through experiments
with AdaptiveStep-trained PRMs in mathematical reasoning and code generation
tasks. Experimental results indicate that the outcome PRM achieves
state-of-the-art Best-of-N performance, surpassing greedy search strategy with
token-level value-guided decoding, while also reducing construction costs by
over 30% compared to existing open-source PRMs. In addition, we provide a
thorough analysis and case study on the PRM's performance, transferability, and
generalization capabilities.
AdaptiveStep improves reward model training by dynamically segmenting reasoning steps based on model confidence, enhancing performance in mathematical reasoning and code generation without manual annotation.
Process Reward Models AdaptiveStep rule-based techniques placeholder tokens fixed size decision points model confidence downstream tasks reward model learning mathematical reasoning code generation Best-of-N performance greedy search strategy token-level value-guided decoding construction costs