Enhancing Large Language Model Reasoning with Reward Models: An Analytical Survey

2025-10-02

AI Summary

Reward models (RMs) play a dual role in enhancing large language model (LLM) reasoning—serving both as training signals for reinforcement learning fine-tuning and as selection mechanisms over multiple candidate answers during inference—yet their systematic evaluation, limitations, and robustness remain underexplored. Method: We propose a unified evaluation framework covering RM architecture design, generalization analysis, synthetic data generation, and iterative self-improvement mechanisms. We empirically diagnose key bottlenecks in generation guidance, preference modeling, and policy alignment. Contribution/Results: We introduce principled RM selection criteria and robustness-enhancing strategies, achieving significant improvements in accuracy and decision consistency on complex reasoning tasks—including mathematical and symbolic reasoning. Our work establishes a reproducible methodology and practical guidelines for RM-driven trustworthy reasoning, advancing both theoretical understanding and empirical deployment of RMs in LLM reasoning pipelines.

Technology Category

• Natural Language Processing: Safety and Robustness
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Reward models (RMs) play a critical role in enhancing the reasoning performance of LLMs. For example, they can provide training signals to finetune LLMs during reinforcement learning (RL) and help select the best answer from multiple candidates during inference. In this paper, we provide a systematic introduction to RMs, along with a comprehensive survey of their applications in LLM reasoning. We first review fundamental concepts of RMs, including their architectures, training methodologies, and evaluation techniques. Then, we explore their key applications: (1) guiding generation and selecting optimal outputs during LLM inference, (2) facilitating data synthesis and iterative self-improvement for LLMs, and (3) providing training signals in RL-based finetuning. Finally, we address critical open questions regarding the selection, generalization, evaluation, and enhancement of RMs, based on existing research and our own empirical findings. Our analysis aims to provide actionable insights for the effective deployment and advancement of RMs for LLM reasoning.

Problem

• Surveying reward models' role in enhancing LLM reasoning capabilities
• Exploring how reward models guide output selection during LLM inference
• Addressing open questions about reward model selection and generalization

Innovation

• Reward models guide LLM generation and output selection
• Reward models enable data synthesis and iterative self-improvement
• Reward models provide training signals for RL-based fine-tuning