However, the combinatorial number of variable subsets prevents direct application of typical RL algorithms.

The task is to i) predict the unknown parameters, then ii) solve the optimization problem using the predicted parameters, such that the resulting solutions are good even under true parameters.

The task is to i) predict the unknown parameters, then ii) solve the optimization problem using the predicted parameters, such that the resulting solutions are good even under true parameters.

How do we program home robots to perform a wide variety of personalized everyday tasks?

Rashomon sets can be large in size and complicated in structure, particularly for highly nonlinear function classes that allow complexinteraction terms, such asdecision trees.

Motivated by these limitations,weintroduceahybridapproach,whereLLMcompletionsforagiven taskareusedtolearn atask-specific, context-free surrogatemodel, which isthen used to guide program synthesis.