AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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FlexLoop is proposed, a novel post-training framework that converts pretrained fixed-depth looped policies into depth-elastic policies that supports reliable inference across recurrent depths and enables state-wise adaptive inference through recurrent-depth consistency.
The proposed Module Level Reward Evolution Framework integrates three mechanisms: reflection-based refinement, hybrid credit assignment, and a merge strategy with rollback, which together improve the effectiveness and robustness of reward optimization.
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