Advancing Strategic Decision Excellence through Self Play Reinforcement Learning Frameworks Leveraging Large Language Models for Recursive Policy Improvement - Nathaniel Blackwood (Computational Intelligence Systems, 2026)
The integration of Large Language Models (LLMs) into autonomous decision-making frameworks represents a paradigm shift in computational intelligence, transitioning from static pattern recognition to dynamic, strategic reasoning. This research explores the development and systemic implications of self-play reinforcement learning frameworks designed to achieve decision excellence through recursive policy improvement. By utilizing LLMs as both the agent and the environment in a self-evolving loop, these frameworks facilitate a sophisticated internal dialogue that simulates complex strategic scenarios, allowing the system to refine its heuristics without human intervention. The study focuses on the architectural trade-offs inherent in large-scale deployments, specifically addressing the balance between computational intensity and the depth of recursive reasoning. Furthermore, the paper examines the socio-technical dimensions of such systems, including the governance of autonomous strategic policies, the robustness of decision-making under adversarial conditions, and the ethical imperatives of fairness and transparency in automated governance. Through a comprehensive analysis of multi-agent interactions and linguistic feedback loops, the research demonstrates how recursive self-improvement can mitigate traditional bottlenecks in reinforcement learning, such as data scarcity and reward hacking. The findings suggest that while self-play LLM frameworks offer unprecedented potential for strategic optimization in engineering and socio-technical infrastructures, they necessitate rigorous oversight mechanisms to prevent policy drift and ensure alignment with human values.