The research landscape in the integration of Large Language Models (LLMs) with reinforcement learning (RL) is rapidly evolving, with a strong emphasis on enhancing adaptability, efficiency, and robustness. Recent advancements focus on developing frameworks that enable LLMs to dynamically adjust their behavior based on feedback, moving beyond static prompts to more sophisticated, information-seeking strategies. These approaches leverage principles from active inference and thermodynamic modeling to create adaptive agents capable of navigating complex, high-dimensional environments. Additionally, there is a growing interest in leveraging LLMs for more effective reward redistribution in RL, addressing challenges related to delayed and sparse feedback through innovative credit assignment mechanisms. The co-evolution of reward functions and policies is also emerging as a promising direction, enhancing the autonomous skill acquisition of intelligent systems with minimal human intervention. These developments collectively push the boundaries of what LLMs can achieve in dynamic and real-world scenarios, emphasizing the importance of continuous learning and adaptation. Notably, papers introducing active inference frameworks and novel reward-policy co-evolution strategies stand out for their innovative approaches to enhancing LLM adaptability and efficiency.