Gome reaches 35.1% any-medal rate on MLE-Bench by mapping reasoning to gradient-based updates, outperforming tree search once models are sufficiently capable.
Loss surfaces, mode connectivity, and fast ensembling of dnns.Advances in neural information processing systems, 31, 2018
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FedUCA formalizes the server as an optimizer that uses utility-constrained stochastic aggregation to maximize client retention and global performance in heterogeneous federated learning.
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Reasoning as Gradient: Scaling MLE Agents Beyond Tree Search
Gome reaches 35.1% any-medal rate on MLE-Bench by mapping reasoning to gradient-based updates, outperforming tree search once models are sufficiently capable.
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Federated Learning by Utility-Constrained Stochastic Aggregation for Improving Rational Participation
FedUCA formalizes the server as an optimizer that uses utility-constrained stochastic aggregation to maximize client retention and global performance in heterogeneous federated learning.