To date, the multi-objective optimization literature has mainly focused on conflicting objectives, studying the Pareto front or requiring users to balance trade-offs. Yet, in machine learning practice, there are many scenarios where such conflict does not take place. Recent findings from multi-task learning, reinforcement learning, and LLMs show that diverse related tasks can enhance performance across objectives simultaneously. Despite this evidence, such phenomenon has not been examined from an optimization perspective. This leads to a lack of generic gradient-based methods that can scale to scenarios with a large number of related objectives. To address this gap, we introduce the Aligned Multi-Objective Optimization framework, propose the \texttt{AMOOO} algorithm, and provide theoretical guarantees of its superior performance compared to naive approaches.