POP: Prior-fitted Optimizer Policies

• URL: http://arxiv.org/abs/2602.15473v1
• Date: Tue, 17 Feb 2026 10:27:07 GMT
• Title: POP: Prior-fitted Optimizer Policies
• Authors: Jan Kobiolka, Christian Frey, Gresa Shala, Arlind Kadra, Erind Bedalli, Josif Grabocka,
• Abstract summary: We introduce POP (Prior Policies Policies), a meta-learned model that predicts coordinate step-wise on contextual information.<n>Our model is learned on millions of synthetic optimization problems sampled from both nonfitted objectives.
• Score: 20.784587787548436
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Optimization refers to the task of finding extrema of an objective function. Classical gradient-based optimizers are highly sensitive to hyperparameter choices. In highly non-convex settings their performance relies on carefully tuned learning rates, momentum, and gradient accumulation. To address these limitations, we introduce POP (Prior-fitted Optimizer Policies), a meta-learned optimizer that predicts coordinate-wise step sizes conditioned on the contextual information provided in the optimization trajectory. Our model is learned on millions of synthetic optimization problems sampled from a novel prior spanning both convex and non-convex objectives. We evaluate POP on an established benchmark including 47 optimization functions of various complexity, where it consistently outperforms first-order gradient-based methods, non-convex optimization approaches (e.g., evolutionary strategies), Bayesian optimization, and a recent meta-learned competitor under matched budget constraints. Our evaluation demonstrates strong generalization capabilities without task-specific tuning.

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