Optimization algorithms stop being rewritten each time: treating iteration trajectories as experience yields a model that transfers across problems and larger dimensions
Iterative optimization algorithms can be turned into trainable models instead of being repeatedly rewritten as programs. The approach treats algorithm execution trajectories as computational experience, models optimization autoregressively so that the preceding generation forms the computational context for the next population, and uses a dimension-independent population-temporal architecture to capture both population interactions and iterative dynamics. The model first acquires search knowledge by imitation from heterogeneous teacher algorithms, then improves itself directly through optimization feedback, consolidating heterogeneous search knowledge into a single model that transfers to unseen, substantially larger dimensions.