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Stony Brook University
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Serverless computing slashes RLHF costs by up to 45% while boosting speed by 35%, thanks to a new framework that dynamically scales resources and avoids redundant computation.
Personalized federated learning can boost VLN performance by up to 7.8% in trajectory fidelity and converge 1.38x faster, by selectively fusing parameters in environment-sensitive layers.
