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The results suggest that tied-$\beta$ Adam admits a simple and approximately stable ratio structure underlying its adaptive behavior and demonstrate its utility for both analysis and efficient implementation.
This work rigorously establishes trainability of Low Rank Adaptation models under stochastic methods, and proves that for a class of mild regularizations, the empirical regression loss on a attention layer and LoRA on a shallow neural net, both induce Poincar's inequality for the corresponding Gibbs'asure.
A novel framework is proposed that integrates MPC with RL in a sequence decision-making framework and leverages a curvature-aware optimization to efficiently tackle non-convex loss landscapes and achieves higher returns and faster convergence.
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