AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work proposes ARMOR (Anchor Rollout and Mixed Optimization for RL), a framework that shifts the paradigm from passive penalty to active sample stabilization, enabling sustained performance improvements over extended training horizons.
SEED (SElf-Evolving On-Policy Distillation), a self-evolving framework that converts completed on-policy trajectories into training-time hindsight skills and distills their behavioral effect back into the policy model, is proposed.
This work proposes Experience-Augmented Policy Optimization (EAPO), which leverages a prior RL-optimized policy as an action-level experience prior and selectively injects experience at critical decision points during rollout to ensure stable and unbiased learning from experience-augmented rollouts.
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