Improving Exploration Efficiency in Complex Reasoning Tasks via Guided Reinforcement Learning and Large Language Model Heuristic Search Strategies

Abstract

The rapid evolution of artificial intelligence has transitioned from simple pattern recognition toward complex reasoning tasks that require long-horizon planning and multi-step cognitive processing. While large language models have demonstrated remarkable zero-shot capabilities, their performance in high-dimensional state spaces is often constrained by the inefficiency of stochastic exploration. Traditional reinforcement learning approaches frequently encounter the curse of dimensionality and the sparsity of reward signals, leading to computational bottlenecks and suboptimal convergence. This paper investigates a novel architectural framework that integrates guided reinforcement learning with large language model heuristic search strategies to enhance exploration efficiency in complex reasoning environments. By leveraging the semantic prior knowledge of language models as a high-level heuristic guide, the proposed system constrains the search space to more plausible trajectories while allowing reinforcement learning agents to refine local execution policies. This research emphasizes the system-level trade-offs between computational overhead and reasoning accuracy, addressing critical infrastructure requirements and deployment strategies for scalable intelligence. Furthermore, the discussion extends to the socio-technical implications of such systems, including robustness against adversarial manipulation, fairness in automated decision-making, and the governance frameworks necessary to oversee autonomous reasoning infrastructures. Through a comprehensive analysis of structural trade-offs and deployment sustainability, this study provides a roadmap for developing more efficient, reliable, and interpretable reasoning agents capable of operating within sophisticated real-world infrastructures.