ContextPRM: Leveraging Contextual Coherence for multi-domain Test-Time Scaling

• URL: http://arxiv.org/abs/2509.24460v1
• Date: Mon, 29 Sep 2025 08:40:46 GMT
• Title: ContextPRM: Leveraging Contextual Coherence for multi-domain Test-Time Scaling
• Authors: Haotian Zhang, Liu Liu, Baosheng Yu, Jiayan Qiu, Likang Xiao, Yanwei Ren, Quan Chen, Xianglong Liu,
• Abstract summary: Process reward models (PRMs) have demonstrated significant efficacy in enhancing the mathematical reasoning capabilities of large language models (LLMs) by leveraging test-time scaling (TTS)<n>We shift the learning objective from verifying domain-specific knowledge to modeling domain-agnostic logical flow.<n>Our approach is realized through a novel data annotation and training framework, which enhances the model's generalization capabilities across diverse domains.
• Score: 38.779046730647856
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Process reward models (PRMs) have demonstrated significant efficacy in enhancing the mathematical reasoning capabilities of large language models (LLMs) by leveraging test-time scaling (TTS). However, while most PRMs exhibit substantial gains in mathematical domains, the scarcity of domain-specific training data and knowledge-based learning patterns limits their generalization ability when faced with other domains. To address this limitation, we shift the learning objective from verifying domain-specific knowledge to modeling domain-agnostic logical flow. Centering on contextual coherence between chain-of-thought (CoT) steps, our approach is realized through a novel data annotation and training framework, which enhances the model's generalization capabilities across diverse domains. For instance, our resulting model, ContextPRM, achieves a notable 6.5% average accuracy improvement over the majority voting baseline via weighted majority voting across nine non-mathematical domains in MMLU-Pro, including law, history, and philosophy, significantly surpassing the 2.2% improvement from VersaPRM and 0.5% gains from other mathematics-focused PRMs, demonstrating consistent performance across both mathematical and non-mathematical domains.

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