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Modularity in neural architectures can dramatically improve compositional learning, but only in lower-dimensional settings where task representations are rich and nuanced.
LLMs trained with Vector Policy Optimization (VPO) learn to produce diverse solutions that unlock previously unsolvable problems in evolutionary search, outperforming models optimized for single scalar rewards.
