Facilitating Cross-Domain Reasoning Generalization through Conservative Offline Reinforcement Learning Leveraging Pre-trained Large Language Model Representations
DOI:
https://doi.org/10.66280/cis.v1i1.196Abstract
The rapid expansion of artificial intelligence into critical infrastructure and socio-technical systems necessitates a transition from narrow task-specific models to resilient agents capable of cross-domain reasoning. Current paradigms often struggle with the "distributional shift" encountered when moving from controlled training environments to high-stakes, real-world deployment. This paper investigates a novel framework for facilitating cross-domain reasoning generalization by integrating Conservative Offline Reinforcement Learning with the latent semantic representations inherent in pre-trained Large Language Models. Unlike online reinforcement learning, which requires continuous environmental interaction and carries significant safety risks in physical systems, our approach utilizes static, multi-domain datasets to derive robust decision-making policies. By leveraging the high-dimensional world knowledge embedded within pre-trained language architectures, the system can map abstract reasoning patterns across disparate domains, such as transitioning from logistics optimization to energy grid management. We provide a comprehensive system-level analysis focusing on structural trade-offs, architecture, and the governance of these hybrid models. Furthermore, we address the ethical implications of deploying autonomous reasoning systems in public infrastructure, emphasizing the need for conservative value estimation to prevent catastrophic failures. Our findings suggest that the intersection of offline learning and linguistic representation provides a sustainable and robust pathway for building generalized intelligent systems that align with complex human institutional frameworks.
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