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 on inputs outside the offline dataset. Prior approaches addressing this challenge have primarily focused on learning robust surrogate models. However%2C their search strategies are derived from the surrogate model rather than the actual offline data. To fill this important gap%2C we introduce a new learning-to-search perspective for offline optimization by reformulating it as an offline reinforcement learning problem. Our proposed policy-guided gradient search approach explicitly learns the best policy for a given surrogate model created from the offline data. Our empirical results on multiple benchmarks demonstrate that the learned optimization policy can be combined with existing offline surrogates to significantly improve the optimization performance.?quality=80&w=800)
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