Cross-Domain Reasoning Transfer via Reinforcement Learning Alignment of Large Language Models
Keywords:
cross-domain reasoning transfer; reinforcement learning alignment; large language models; socio-technical systems; AI governance; robustness; fairness; infrastructureAbstract
Large language models have become general-purpose computational infrastructures, yet their ability to transfer reasoning across domains remains uneven and poorly governed. This paper examines cross-domain reasoning transfer through the lens of reinforcement learning alignment, arguing that transfer is not merely a property of model scale or prompt design but a socio-technical outcome shaped by reward modeling, deployment architecture, feedback infrastructure, evaluation regimes, and policy choices. The analysis begins with conceptual foundations, distinguishing procedural transfer from surface-level pattern reuse and explaining why reinforcement learning alignment can either amplify or constrain cross-domain generalization. It then examines architectural choices, including multi-task reward models, domain-conditioned policies, verifier-assisted reasoning, retrieval augmentation, and path filtering for small-parameter systems. Particular attention is given to the structural trade-offs between generality and specialization, sample efficiency and safety, modularity and integration, and centralized training and distributed inference. The paper further considers infrastructure and sustainability, showing how feedback collection, distributed serving, monitoring, and continual alignment create long-term technical debt and energy burdens. Robustness, fairness, and evaluation are treated as coupled system properties rather than post hoc checks, because reasoning transfer can propagate errors, biases, and reward hacking across institutional boundaries. Governance and policy implications are explored through transparency, auditability, liability, procurement, and human-centered oversight. Case illustrations from medicine, law, education, software engineering, and scientific research clarify how domain characteristics alter transfer risk. The paper concludes that durable cross-domain reasoning transfer requires reinforcement learning alignment to be embedded in accountable, reversible, and resource-aware infrastructures rather than treated as a single training intervention.
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