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This paper empirically studies how the composition of training data (curriculum) affects the skills learned by RL-based memory agents in multi-session question answering. It finds that curriculum composition acts as a fine-grained lever on specialization, with mixed benchmarks yielding the best overall performance and narrow out-of-domain sets transferring targeted temporal reasoning skills.