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The implementation lottery reveals that relying on a single run can mislead research conclusions, with winner reversals occurring in up to 43.6% of cases.
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.
Continual learning for LLM agents hits a wall: scaling models doesn't reliably improve skill generation, and self-feedback can lead to recursive drift.
Forget full retraining: intelligently selecting data subsets using gradient-based representations can keep your generative recommender fresh and robust to drift.
