AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work introduces a novel combinatorial dimension of the reward class which is called the alignment dimension, and shows that it completely characterizes the alignment learnability --- a reward class is alignment learnable if and only if its alignment dimension is finite.
RD-MCTS, a Monte Carlo Tree Search framework that incorporates diagram-derived structural priors, constraint-consistent state transitions, and inference-time step-level process rewards is proposed, aimed at improving constraint-consistent reasoning on high-difficulty questions.
This work improves the upper bound to match the best known lower bound, thus establishing the optimal error guarantee for learning under Tsybakov noise.
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