Linking Process to Outcome: Conditional Reward Modeling for LLM Reasoning

• URL: http://arxiv.org/abs/2509.26578v1
• Date: Tue, 30 Sep 2025 17:38:45 GMT
• Title: Linking Process to Outcome: Conditional Reward Modeling for LLM Reasoning
• Authors: Zheng Zhang, Ziwei Shan, Kaitao Song, Yexin Li, Kan Ren,
• Abstract summary: Process Reward Models (PRMs) aim to guide their step-by-step reasoning toward a final answer.<n>Existing PRMs fail to capture inter-step dependencies, or struggle to align process rewards with the final outcome.<n>We propose Conditional Reward Modeling that frames reasoning as a temporal process leading to a correct answer.
• Score: 30.302863491794543
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Process Reward Models (PRMs) have emerged as a promising approach to enhance the reasoning capabilities of large language models (LLMs) by guiding their step-by-step reasoning toward a final answer. However, existing PRMs either treat each reasoning step in isolation, failing to capture inter-step dependencies, or struggle to align process rewards with the final outcome. Consequently, the reward signal fails to respect temporal causality in sequential reasoning and faces ambiguous credit assignment. These limitations make downstream models vulnerable to reward hacking and lead to suboptimal performance. In this work, we propose Conditional Reward Modeling (CRM) that frames LLM reasoning as a temporal process leading to a correct answer. The reward of each reasoning step is not only conditioned on the preceding steps but also explicitly linked to the final outcome of the reasoning trajectory. By enforcing conditional probability rules, our design captures the causal relationships among reasoning steps, with the link to the outcome allowing precise attribution of each intermediate step, thereby resolving credit assignment ambiguity. Further, through this consistent probabilistic modeling, the rewards produced by CRM enable more reliable cross-sample comparison. Experiments across Best-of-N sampling, beam search and reinforcement learning demonstrate that CRM consistently outperforms existing reward models, offering a principled framework for enhancing LLM reasoning. In particular, CRM is more robust to reward hacking and delivers stable downstream improvements without relying on verifiable rewards derived from ground truth.

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