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FedTOPS (Federated Teacher-guided On-Policy Scaling), which reuses teacher feedback on current trajectories to adapt the FedAvg update magnitude under clientwise predictive-change constraints is proposed.
DAGPipe is proposed, a novel framework that jointly learns both the topology and parameters of data preparation pipelines in a fully differentiable manner and attains the best average rank among state-of-the-art preprocessing methods and exceeds the average baseline on every dataset.
PreRet, a framework that jointly optimizes preprocessing pipeline selection and neural retrieval through differentiable optimization through differentiable optimization, is proposed, enabling end-to-end gradient-based learning over a search space of 700 configurations spanning text cleaning, pre-tokenization, chunking,...
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