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Hyperparameter selection can now be backed by formal statistical guarantees, transforming a traditionally heuristic process into a reliable, principled approach.
Now you can adaptively control false discovery rates in your selections *after* seeing the data, letting you balance selection size and FDR based on observed evidence and downstream needs.
Stop training your reward models on easy examples: MARS boosts reward modeling performance by focusing augmentation on the ambiguous preference pairs where the model struggles most.
