Adaptive Reward Modeling for Large Language Model Reasoning Using Response Quality Prediction and Explainable Machine Learning Techniques
Keywords:
adaptive reward modeling, large language models, reasoning, response quality prediction, explainable machine learning, reinforcement learning from human feedback, model alignment, socio-technical systemsAbstract
The rapid advancement of large language models has demonstrated remarkable capabilities in complex reasoning tasks, yet the design of effective reward functions remains a central challenge in aligning model behavior with desired outcomes. Traditional reward modeling in reinforcement learning from human feedback relies on static, human-annotated preferences that are costly to obtain and often fail to capture the nuanced quality of multi-step reasoning. This paper proposes an adaptive reward modeling framework that integrates response quality prediction with explainable machine learning techniques to dynamically assess and reward reasoning outputs. The framework leverages predictive models trained on diverse quality indicators to generate continuous reward signals, while explainability methods such as SHAP and LIME provide interpretable attributions that enhance transparency and trust. We examine the system-level architecture required for deployment, including data pipelines, inference infrastructure, and feedback loops that enable continuous adaptation. The approach introduces structural trade-offs between predictive accuracy, computational overhead, and explainability fidelity. We analyze robustness and fairness implications, showing how adaptive reward signals can mitigate biases present in static reward models but may introduce new distributional dependencies. Governance and policy considerations are discussed in the context of model alignment, accountability, and the need for auditable reward generation processes. Cross-domain comparisons with traditional reward modeling, inverse reinforcement learning, and process-supervision methods are provided to contextualize the contribution. Case illustrations from mathematical reasoning, code generation, and commonsense reasoning demonstrate the framework’s versatility. The paper concludes with forward-looking perspectives on sustainable reward infrastructure and the role of explainable AI in shaping future alignment strategies.
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