Trial and Error

Trial and error, a fundamental learning process, is being actively investigated across diverse fields, focusing on how to efficiently leverage both successes and failures to improve performance. Current research emphasizes developing algorithms that enable agents, from robots to large language models, to intelligently manage exploration and exploitation during trial-and-error learning, incorporating techniques like Bellman-Guided Retrials and Boosting of Thoughts to enhance efficiency and success rates. This research is significant for improving the robustness and safety of AI systems in complex real-world applications, particularly in robotics and decision-making under uncertainty, while also offering insights into human learning processes.