AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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An Online Mirror Descent framework with adaptive proximal functions for matrix-valued parameters, providing a principled methodology for deriving matrix-aware adaptive optimization through online regret minimization and yields Row-wise Matrix AdaGrad and Column-wise Matrix AdaGrad as concrete instantiations with regret...
Value Flattening is identified as an important yet overlooked failure mode of critic learning in standard PPO and a simple sparse supervision strategy can mitigate it; SParse Proximal Policy Optimization is introduced, which applies the value loss to only a few well-separated states in each response to mitigate both ef...
This work proposes dynamic token-choice routing for looped transformers, enabling each token to adaptively determine its own number of loop iterations based on its hidden state, which can improve the token generation accuracy and validate the effectiveness of token-choice router and recursion-wise KV cache.
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