Temporal Consistency for LLM Reasoning Process Error Identification
Verification is crucial for effective mathematical reasoning. We present a
new temporal consistency method where verifiers iteratively refine their
judgments based on the previous assessment. Unlike one-round verification or
multi-model debate approaches, our method leverages consistency in a sequence
of self-reflection actions to improve verification accuracy. Empirical
evaluations across diverse mathematical process error identification benchmarks
(Mathcheck, ProcessBench, and PRM800K) show consistent performance improvements
over baseline methods. When applied to the recent DeepSeek R1 distilled models,
our method demonstrates strong performance, enabling 7B/8B distilled models to
outperform all 70B/72B models and GPT-4o on ProcessBench. Notably, the
distilled 14B model with our method achieves performance comparable to
Deepseek-R1. Our codes are available at
https://github.com/jcguo123/Temporal-Consistency
67da454fd5132b0eebd066ff
Jiacheng Guo
admin_assigned
2025-03-19T13:43:08.518Z
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Jiacheng Guo
user
gjcjcg
67da454fd5132b0eebd06700
Yue Wu
67da454fd5132b0eebd06701
Jiahao Qiu
claimed_verified
2025-05-28T19:15:05.409Z
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Jiahao Qiu
user
jiahaoq
67da454fd5132b0eebd06702
Kaixuan Huang
admin_assigned
2025-03-19T13:43:51.256Z
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huangkaixuan
user
huangkaixuan
67da454fd5132b0eebd06703
Xinzhe Juan
admin_assigned
2025-03-19T13:43:39.557Z
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Xinzhe Juan
user
ChrisJuan
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Ling Yang
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Mengdi Wang
admin_assigned
2025-03-19T13:43:31.791Z
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Mengdi Wang
user
Edify-Kd2024
Temporal Consistency for LLM Reasoning Process Error Identification
Verification is crucial for effective mathematical reasoning. We present a
new temporal consistency method where verifiers iteratively refine their
judgments based on the previous assessment. Unlike one-round verification or
multi-model debate approaches, our method leverages consistency in a sequence
of self-reflection actions to improve verification accuracy. Empirical
evaluations across diverse mathematical process error identification benchmarks
(Mathcheck, ProcessBench, and PRM800K) show consistent performance improvements
over baseline methods. When applied to the recent DeepSeek R1 distilled models,
our method demonstrates strong performance, enabling 7B/8B distilled models to
outperform all 70B/72B models and GPT-4o on ProcessBench. Notably, the
distilled 14B model with our method achieves performance comparable to
Deepseek-R1. Our codes are available at
https://github.com/jcguo123/Temporal-Consistency
A new iterative self-reflection method for verification improves mathematical reasoning accuracy over baselines, especially when applied to distilled models.
temporal consistency iterative refinement self-reflection verification accuracy Mathcheck ProcessBench PRM800K DeepSeek R1 distilled models GPT-4o