AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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STAR is proposed, an efficient PbRL method that integrates preference margin regularization and policy regularization that improves feedback efficiency and facilitates more robust reward and value function learning.
HyMem is a hierarchical framework that explicitly separates the agent's context into distinct functional layers to separate high-level planning from execution and complex analysis, allowing the model to maintain focus and accuracy across complex, long-horizon tasks.
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