The smoothness assumption posits that small changes in input lead to small changes in output, a foundational principle in machine learning and optimization. It underpins the design of algorithms that rely on gradient-based methods, ensuring that they can efficiently navigate towards optimal solutions by assuming a predictable, gradual landscape.