AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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ConvMem is a training-free, highly parallelizable framework that reformulates long-context reasoning as a hierarchical convolution, Inspired by CNNs, that outperforms training-free baselines and avoids the risk of overfitting to parametric priors often observed in RL-trained models on out-of-distribution tasks.
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.
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