Streamlining Real Time Decision Reasoning via Reinforcement Learning Driven Large Language Model Agents for Complex Task Planning and Adaptation - Alexander Ellison; Stephen Carmichael; Eric Whitman (Computational Intelligence Systems, 2026)
The integration of Large Language Models (LLMs) into autonomous decision-making frameworks has catalyzed a shift in how socio-technical systems manage complex task planning. However, the inherent latency and stochastic nature of autoregressive generation often impede real-time responsiveness in dynamic environments. This research paper explores the architectural convergence of Reinforcement Learning (RL) and LLM-based agents to streamline decision reasoning. By shifting the paradigm from static prompting to dynamic, policy-driven adaptation, we investigate how reinforcement learning can refine the internal latent reasoning paths of agents to prioritize efficiency and robustness. The study emphasizes system-level trade-offs, particularly the tension between computational intensity and decision fidelity. We examine the infrastructure required to deploy these agents at scale, focusing on the governance of autonomous reasoning and the ethical implications of RL-tuned linguistic agents. Through an analysis of deployment strategies and sustainability, the paper argues that a decentralized, RL-augmented framework significantly enhances the reliability of automated systems in high-stakes environments. The discussion extends to policy implications, suggesting that as these agents become embedded in critical infrastructure, new regulatory standards for algorithmic transparency and fairness are essential to mitigate systemic risks.