Abstract: This talk will introduce the foundations of behavior cloning - a setting in which sequential decision making is trained via supervision from expert demonstration. Our tutorial will focus on the role of problem horizon - the number of decision making steps - and the possibility of error being amplified as horizon increases. We will review classical results for mitigating error amplification, such as the Dagger algorithm, as well as more modern contributions that study the role of compounding in contemporary applications, such as in large language models and robotics.