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Introduces SPO, a stochastic search framework for automatic prompt optimization, with three strategies including SAGE, an agent-guided multi-agent pipeline. Evaluated on benchmarks and deployed on a mental-health chatbot, showing improvements in retention through continuous optimization.
This paper proposes DOMOO, a diversity-driven offline multi-objective optimization method that uses accumulative risk control and nested Pareto set learning to address out-of-distribution issues, achieving superior convergence and diversity on benchmarks.
The paper proposes a permutation-invariant Bayesian optimization method based on Optimal Transport for optimizing offshore wind farm layouts, which reduces computation time by half and yields better layouts compared to vanilla Bayesian optimization.
OpenAI presents evolution strategies (ES) as a scalable black-box optimization alternative to reinforcement learning for training neural network policies. ES simplifies the optimization problem by treating policy training as a stochastic parameter search that repeatedly samples and selects better parameter configurations based on reward feedback.