Optimizing Process Based Reward Models through Reinforcement Learning for Verifiable Multi Step Reasoning in Large Language Model Architectures
DOI:
https://doi.org/10.66280/ijair.v1i2.156Keywords:
Large Language Models, Process Based Reward Models, Reinforcement Learning, Multi-Step Reasoning, System Architecture, Algorithmic Governance, Verifiable AI.Abstract
The evolution of large language models has transitioned from simple predictive text completion toward complex, multi-step cognitive reasoning. However, traditional outcome-based reward models, which evaluate only the final correctness of a solution, often fail to identify logical fallacies or "hallucinations" occurring within intermediate steps. This paper explores the optimization of Process-Based Reward Models (PRMs) through reinforcement learning to enhance the verifiability and robustness of multi-step reasoning in large-scale model architectures. Unlike traditional approaches, PRMs assign value to each distinct stage of a reasoning chain, providing a more granular signal for training. This study analyzes the structural trade-offs involved in deploying these models at scale, focusing on the infrastructure requirements, the computational overhead of step-wise verification, and the socio-technical implications of automated reasoning governance. We argue that while process-based supervision significantly improves the reliability of models in high-stakes domains such as law, medicine, and engineering, it introduces unique challenges regarding system latency and the sustainability of human-in-the-loop feedback loops. By integrating reinforcement learning with process-oriented feedback, developers can foster a more transparent AI ecosystem where the path to a conclusion is as scrutinized as the conclusion itself. The discussion encompasses the broader implications for algorithmic fairness, the reduction of black-box opacity, and the policy frameworks necessary to govern verifiable machine intelligence in modern socio-technical infrastructures.
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