AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work proposes RetireOPD (Self-Retiring On-Policy Distillation), which first optimizes a decoupled, skill-conditioned teacher with environment rewards and then trains a skill-free student jointly with RL and OPD.
SERTPO (Self-Evolving Rubric Policy Optimization), which replaces answer voting with a closed loop that co-evolves response evidence, query-specific rubrics, and policy parameters, and supports OOD transfer and continued cross-benchmark evolution.
Test-Time Policy Optimization is proposed, an asymmetric objective that distills agreeing rollouts via OPSD and penalizes disagreeing rollouts with Grouped RL and Token-level selection further refines both branches: distillation down-weights already-converged positions, while RL penalizes only confident errors.
The proposed DaViNCi is the first outdoor VLN dataset that simultaneously introduces both continuous and dynamic factors, and the practical value of DaViNCi in advancing outdoor VLN toward more realistic environments is demonstrated.
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