Sarcouncil Journal of Engineering and Computer Sciences

Sarcouncil Journal of Engineering and Computer Sciences

An Open access peer reviewed international Journal
Publication Frequency- Monthly
Publisher Name-SARC Publisher

ISSN Online- 2945-3585
Country of origin-PHILIPPINES
Impact Factor- 3.7
Language- English

Keywords

Editors

Adaptive Reinforcement Learning Framework for Enterprise Data Integration in LLM Training

Keywords: Reinforcement Learning, Enterprise Data Integration, Large Language Models, Adaptive Data Pipelines, Automated Decision-Making.

Abstract: This article presents a comprehensive framework for leveraging Reinforcement Learning (RL) in enterprise data integration for Large Language Model training. Traditional Extract, Transform, Load approaches face significant limitations when handling the complexity, scalability, and manual intervention requirements of modern data environments. The proposed RL-driven architecture enables systems to learn optimal integration strategies through continuous interaction with dynamic environments, addressing these persistent challenges through adaptive decision-making. The article explores the theoretical foundations of RL for data integration, details a modular system architecture, and examines practical application scenarios across retail, healthcare, autonomous vehicles, and cloud-based workflows. Implementation considerations including technical requirements, evaluation frameworks, resource management, and compliance factors are thoroughly addressed. The integration of RL with complementary technologies such as federated learning, transfer learning, and large language models points toward a future where enterprise data integration transitions from static, maintenance-intensive infrastructures to dynamic, self-optimizing ecosystems that continuously enhance data utility.

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