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Diminishing returns in parallel sampling can be overcome by generating diverse initial queries, leading to substantial performance gains in multi-hop question answering.
Recycling zero-variance queries during training can boost model performance, enabling a smaller 1.7B parameter model to match or exceed the accuracy of larger counterparts.
