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Regression-error guarantees for learning operators from dependent sequential data could revolutionize adaptive experimental design and Bayesian optimization.
Decomposing complex, verifiable rewards in LVLM reinforcement fine-tuning provably accelerates convergence and improves generalization, offering a principled alternative to monolithic reward optimization.
