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Randomized LMO methods still face a $1/T^2$ convergence rate bottleneck for strongly convex optimization, even with adaptive oracles.
Sparsity, often viewed as a means for efficiency, actually unlocks deeper, more effective LLMs by taming variance and boosting layer utilization.