The recent advancements in the field of Large Language Models (LLMs) have shown a significant shift towards enhancing the capabilities of autonomous agents through innovative training methodologies and practical considerations. One notable trend is the development of training methods that leverage weakly supervised feedback from LLMs, enabling agents to improve their performance without the need for expert trajectories or definitive environmental feedback. This approach has demonstrated promising results in tasks requiring iterative environmental interaction, suggesting a broader applicability beyond traditional reinforcement learning scenarios. Additionally, there is a growing interest in using LLMs for abstract reasoning and planning tasks, particularly in areas like Elementary Cellular Automata, where Transformers are being trained to generalize and abstract rules governing these systems. The incorporation of future states and rule prediction in the training process has been shown to enhance the models' ability to perform multi-step planning and autoregressive generation, highlighting the potential for improving LLM capabilities through more sophisticated loss functions and model architectures. Furthermore, the practical deployment of LLM-based agents is being addressed with a focus on handling unpredictability and resource management, ensuring that these systems can be effectively integrated into real-world applications. Overall, the field is moving towards more versatile and robust LLM-based agents that can handle a wide range of tasks with improved efficiency and accuracy.