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Selective re-scanning in recurrent networks can drastically reduce memory usage while improving task performance, challenging the conventional wisdom of fixed-size state fidelity.
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.
