AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Sparse Weight Decomposition is proposed, which reparameterizes pretrained linear projections by factorizing each weight matrix into two sparse factors whose shared intermediate coordinates serve as individually addressable circuit units.
An angular effective learning rate is derived that accounts for the parameter-update angle, parameter norm, and update norm, and shows that the conventional norm-based measure is a special case under parameter-update orthogonality.
It is found that OPD transfers a teacher's reasoning behavior rather than its answers to particular problems: training difficulty barely matters, and even problems the teacher never solves are useful.
This work presents a unified black-box RL framework for stable and scalable optimization of general agents through complex harnesses, supporting unified training across heterogeneous execution systems.
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