AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Reward-Aligned Distillation (RAD), a bilevel framework that weights distillation signals based on their predicted effect on the student's post-update reward, supports reward-guided weighting of teacher supervision across model families and tasks.
This work proposes AHEAD, a step-aware framework that matches different supervision sources to different step types and solves tasks within tighter interaction budgets than outcome-only RL and prior self-distillation baselines.
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