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Scaling laws predict how loss decreases with increases in computation. We show, contrary to conventional wisdom, that architectural interventions can modify scaling exponents in pre-training, leading to power-law improvements in performance as computation increases. As an anchoring point, we consider the architectural...
A contextual restless multi-armed bandit (CRMAB) framework in which a grid operator requests load reductions without observing internal job-scheduling decisions is proposed, demonstrating the economic potential of data-center flexibility as a grid service and highlighting the importance of high-quality, open-source AI...
This work introduces a multi-objective optimization framework that formulates routing as an online linear programming with interpretable decision rewards and applies an efficient bid-price control policy based on the online linear programming that admits requests when their SLO-weighted benefit exceeds their shadow pri...
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